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53e9978ab7602d9701f4bc56

This report covers the activities of the 3rd European Lisp Workshop. We introduce the motivation for a workshop focussing on languages in the Lisp family, and mention relevant organisational aspects. We summarize the presentations and discussions, including Nick Levine’s keynote talk, and provide pointers to related work and events.

53e99a04b7602d970225216a

The essence of an application is a set of objects and a set of operations on those objects. The essence of the behavior of an application's user interface is similar to Lisp's read-eval-print loop: the user specifies what he or she wants to do (perhaps via a menu or a dialog or a direct manipulation), the application performs the operation, and then the result of that operation is displayed. The Common Lisp Interface Manager (CLIM) is a system for constructing portable users interfaces in a way that directly connects the objects and operations of an application to the objects of its user interface.

53e9978ab7602d9701f4bc56

This report covers the activities of the 3rd European Lisp Workshop. We introduce the motivation for a workshop focussing on languages in the Lisp family, and mention relevant organisational aspects. We summarize the presentations and discussions, including Nick Levine’s keynote talk, and provide pointers to related work and events.

53e999a5b7602d97021e755f

A prototype for an extensible interactive graphical term manipulation system is presented that combines pattern matching and nondeterministic evaluation to provide a convenient framework for doing tedious algebraic manipulations that so far had to be done manually in a semi-automatic fashion.

53e99a04b7602d970225216a

The essence of an application is a set of objects and a set of operations on those objects. The essence of the behavior of an application's user interface is similar to Lisp's read-eval-print loop: the user specifies what he or she wants to do (perhaps via a menu or a dialog or a direct manipulation), the application performs the operation, and then the result of that operation is displayed. The Common Lisp Interface Manager (CLIM) is a system for constructing portable users interfaces in a way that directly connects the objects and operations of an application to the objects of its user interface.

53e99d7ab7602d9702634286

We propose an effective and efficient traffic-smoothing called the efficient changes and variability bandwidth allocation (ECVBA) scheme. This algorithm not only minimizes the peak rate of a stream but also increases the likelihood of successful VBR stream transmission. The main benefit is that it can immediately release bandwidth to other sites in the network.

53e99a04b7602d970225216a

The essence of an application is a set of objects and a set of operations on those objects. The essence of the behavior of an application's user interface is similar to Lisp's read-eval-print loop: the user specifies what he or she wants to do (perhaps via a menu or a dialog or a direct manipulation), the application performs the operation, and then the result of that operation is displayed. The Common Lisp Interface Manager (CLIM) is a system for constructing portable users interfaces in a way that directly connects the objects and operations of an application to the objects of its user interface.

53e9a6dfb7602d97030121a2

The Chiron-1 user interface system demonstrates key techniques that enable a strict separation of an application from its user interface. These techniques include separating the control-flow aspects of the application and user interface: they are concurrent and may contain many threads. Chiron also separates windowing and look-and-feel issues from dialogue and abstract presentation decisions via mechanisms employing a client-server architecture. To separate application code from user interface code, user interface agents called artists are attached to instances of application abstract data types (ADTs). Operations on ADTs within the application implicitly trigger user interface activities within the artists. Multiple artists can be attached to ADTs, providing multiple views and alternative forms of access and manipulation by either a single user or by multiple users. Each artist and the application run in separate threads of control. Artists maintain the user interface by making remote calls to an abstract depiction hierarchy in the Chiron server, insulting the user interface code from the specifics of particular windowing systems and toolkits. The Chiron server and clients execute in separate processes. The client-server architecture also supports multilingual systems: mechanisms are demonstrated that support clients written in programming languages other than that of the server while nevertheless supporting object-oriented server concepts. The system has been used in several universities and research and development projects. It is available by anonymous ftp.

53e99a04b7602d970225216a

The essence of an application is a set of objects and a set of operations on those objects. The essence of the behavior of an application's user interface is similar to Lisp's read-eval-print loop: the user specifies what he or she wants to do (perhaps via a menu or a dialog or a direct manipulation), the application performs the operation, and then the result of that operation is displayed. The Common Lisp Interface Manager (CLIM) is a system for constructing portable users interfaces in a way that directly connects the objects and operations of an application to the objects of its user interface.

53e9b188b7602d9703c0e258

We describe ESA (for Emacs-Style Application), a library for writing applications with an Emacs look-and-feel within the Common Lisp Interface Manager. The ESA library takes advantage of the layered design of CLIM to provide a command loop that uses Emacs-style multi-keystroke command invocation. ESA supplies other functionality for writing such applications such as a minibuffer for invoking extended commands and for supplying command arguments, Emacs-style keyboard macros and numeric arguments, file and buffer management, and more. ESA is currently used in two major CLIM applications: the Climacs text editor (and the Drei text gadget integrated with the McCLIM implementation), and the Gsharp score editor. This paper describes the features provided by ESA, gives some detail about their implementation, and suggests avenues for further work.

53e99a04b7602d970225216a

The essence of an application is a set of objects and a set of operations on those objects. The essence of the behavior of an application's user interface is similar to Lisp's read-eval-print loop: the user specifies what he or she wants to do (perhaps via a menu or a dialog or a direct manipulation), the application performs the operation, and then the result of that operation is displayed. The Common Lisp Interface Manager (CLIM) is a system for constructing portable users interfaces in a way that directly connects the objects and operations of an application to the objects of its user interface.

53e9ba7db7602d97046a1a2a

The development of user interfaces for large applications is subject to a series of well-known problems including cost, maintainability, and sensitivity to changes in the operating environment. The Chiron user interface development system has been built to address these software engineering concerns. Chiron introduces a series of layers that insulate components of an application from other components that may experience change. To separate application code from user interface code, user interface agents called artists are attached to application abstract data types. Operations on abstract data types within the application implicitly trigger user interface activities. Chiron also provides insulation between the user interface layer and the underlying system; artist code is written in terms of abstract depiction libraries that insulate the code from the specifics of particular windowing systems and toolkits. Concurrency is pervasive in the Chiron architecture. Inside an application there can be multiple execution threads; there is no requirement for a user interface listening/dispatching routine to have exclusive control. Multiple artists can be attached to a single application abstract data type, providing alternative forms of access by a single user or coordinated access and manipulation by multiple users.

53e99a04b7602d970225216a

The essence of an application is a set of objects and a set of operations on those objects. The essence of the behavior of an application's user interface is similar to Lisp's read-eval-print loop: the user specifies what he or she wants to do (perhaps via a menu or a dialog or a direct manipulation), the application performs the operation, and then the result of that operation is displayed. The Common Lisp Interface Manager (CLIM) is a system for constructing portable users interfaces in a way that directly connects the objects and operations of an application to the objects of its user interface.

53e9be79b7602d9704b3a0e0

There is an increasing number of contributions on how to solve the various problems within requirements engineering (RE). The purpose of this paper is to identify the main goals to be reached during the RE process in order to develop a framework for RE. This framework consists of three dimensions: • • the specification dimension • • the representation dimension • • the agreement dimension. We show how this framework can be used to classify and clarify current RE research as well as RE support offered by methods and tools. In addition, the framework can be applied to the analysis of existing RE practise and the establishment of suitable process guidance. Last but not least, the framework offers a first step towards a common understanding of RE.

53e9978db7602d9701f4e881

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e9978db7602d9701f4e881

53e9b159b7602d9703bdde9b

We characterize the transitive closure of the control dependence relation and give an application to the theory of control fow guards. We relate our result to characterizations by Beck et al., by Sarkar, and by Cytron et al., and strengthen a result of the latter concerning dominance frontiers and join sets.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e997e4b7602d9701fdcd0e

Induction variable detection is usually closely tied to the strength reduction optimization. This paper studies induction variable analysis from a different perspective, that of finding induction variables for data dependence analysis. While classical induction variable analysis techniques have been used successfully up to now, we have found a simple algorithm based on the Static Single Assignment form of a program that finds all linear induction variables in a loop. Moreover, this algorithm is easily extended to find induction variables in multiple nested loops, to find nonlinear induction variables, and to classify other integer scalar assignments in loops, such as monotonic, periodic and wrap-around variables. Some of these other variables are now classified using ad hoc pattern recognition, while others are not analyzed by current compilers. Giving a unified approach improves the speed of compilers and allows a more general classification scheme. We also show how to use these variables in data dependence testing.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e997f9b7602d97020031b9

In a compiling system that attempts to improve code for a whole program by optimizing across procedures, the compiler can generate better code for a specific procedure if it knows which variables will have constant values, and what those values will be, when the procedure is invoked. This paper presents a general algorithm for determining for each procedure in a given program the set of inputs that will have known constant values at run time. The precision of the answers provided by this method are dependent on the precision of the local analysis of individual procedures in the program. Since the algorithm is intended for use in a sophisticated software development environment in which local analysis would be provided by the source editor, the quality of the answers will depend on the amount of work the editor performs. Several reasonable strategies for local analysis with different levels of complexity and precision are suggested and the results of a prototype implementation in a vectorizing Fortran compiler are presented.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99804b7602d9702019302

Contification is a compiler optimization that turns a function that always returns to the same place into a continuation. Compilers for functional languages use contification to expose the control-flow information that is required by many optimizations, including traditional loop optimizations. This paper gives a formal presentation of contification in MLton, a whole-program optimizing Standard ML compiler. We present two existing algorithms for contification in our framework, as well as a new algorithm based on the dominator tree of a program's call graph. We prove that the dominator algorithm is optimal. We present benchmark results on realistic SML programs demonstrating that contification has minimal overhead on compile time and significantly improves run time.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99a20b7602d9702277a73

Most modern compilers operate by applying a fixed, program-independent sequence of optimizations to all programs. Compiler writers choose a single "compilation sequence", or perhaps a couple of compilation sequences. In choosing a sequence, they may consider performance of benchmarks or other important codes. These sequences are intended as general-purpose tools, accessible through command-line flags such as -O2 and -O3.Specific compilation sequences make a significant difference in the quality of the generated code, whether compiling for speed, for space, or for other metrics. A single universal compilation sequence does not produce the best results over all programs [8, 10, 29, 32]. Finding an optimal program-specific compilation sequence is difficult because the space of potential sequences is huge and the interactions between optimizations are poorly understood. Moreover, there is no systematic exploration of the costs and benefits of searching for good (i.e., within a certain percentage of optimal) program-specific compilation sequences.In this paper, we perform a large experimental study of the space of compilation sequences over a set of known benchmarks, using our prototype adaptive compiler. Our goal is to characterize these spaces and to determine if it is cost-effective to construct custom compilation sequences. We report on five exhaustive enumerations which demonstrate that 80% of the local minima in the space are within 5 to 10% of the optimal solution. We describe three algorithms tailored to search such spaces and report on experiments that use these algorithms to find good compilation sequences. These experiments suggest that properties observed in the enumerations hold for larger search spaces and larger programs. Our findings indicate that for the cost of 200 to 4,550 compilations, we can find custom sequences that are 15 to 25% better than the human-designed fixed-sequence originally used in our compiler.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99b26b7602d97023be609

This paper describes a new framework of register allocation based on Chaitin-style coloring. Our focus is on maximizing the chances for live ranges to be allocated to the most preferred registers while not destroying the colorability obtained by graph simplification. Our coloring algorithm uses a graph representation of preferences called a Register Preference Graph, which helps find a good register selection. We then try to relax the register selection order created by the graph simplification. The relaxed order is defined as a partial order, represented using a graph called a Coloring Precedence Graph. Our algorithm utilizes such a partial order for the register selection instead of using the traditional simplification-driven order so that the chances of honoring the preferences are effectively increased. Experimental results show that our coloring algorithm is powerful to simultaneously handle spill decisions, register coalescing, and preference resolutions.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e9a7ddb7602d970311df43

We present a flow-sensitive interprocedural constant propagation algorithm, which supports recursion while only performing one flow-sensitive analysis of each procedure. We present experimental results which show that this method finds substantially more constants than previous methods and is efficient in practice. We introduce new metrics for evaluating interprocedural constant propagation algorithms which measure the number of interprocedural constant values that are propagated. We use these metrics to provide further experimental results for our algorithm.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e9979fb7602d9701f70e0e

Many parallel programs are written in SPMD style, i.e. by running the same sequential program on all processes. SPMD programs include synchronization, but it is easy to write incorrect synchronization patterns. We propose a system that verifies a program''s synchronization pattern. We also propose language features to make the synchronization pattern more explicit and easily checked. We have implemented a prototype of our system for Split-C and successfully verified the synchronization structure of realistic programs.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e997a2b7602d9701f729e0

Growing interest in graphics processing units has brought renewed attention to the Single Instruction Multiple Data (SIMD) execution model. SIMD machines give application developers tremendous computational power; however, programming them is still challenging. In particular, developers must deal with memory and control-flow divergences. These phenomena stem from a condition that we call data divergence, which occurs whenever two processing elements (PEs) see the same variable name holding different values. This article introduces divergence analysis, a static analysis that discovers data divergences. This analysis, currently deployed in an industrial quality compiler, is useful in several ways: it improves the translation of SIMD code to non-SIMD CPUs, it helps developers to manually improve their SIMD applications, and it also guides the automatic optimization of SIMD programs. We demonstrate this last point by introducing the notion of a divergence-aware register spiller. This spiller uses information from our analysis to either rematerialize or share common data between PEs. As a testimony of its effectiveness, we have tested it on a suite of 395 CUDA kernels from well-known benchmarks. The divergence-aware spiller produces GPU code that is 26.21% faster than the code produced by the register allocator used in the baseline compiler.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e997bab7602d9701fa3a98

SUMMARY Value numbering is a compiler-based program analysis method that allows redundant computations to be removed. This paper compares hash-based approaches derived from the classic local algorithm1 with partitioning approaches based on the work of Alpern, Wegman, and Zadeck2. Historically, the hash-based algorithm has been applied to single basic blocks or extended basic blocks. We have improved the technique to operate over the routine's dominator tree. The partition ing approach partitions the values in the routine into congruence classes and removes computations when one congruent value dominates another. We have extended this technique to remove computations that define a value in the set of available expressions (AVAIL)3. Also, we are able to apply a version of Morel and Renvoise's partial redundancy elimination4 to remove even more redundancies. The paper presents a series of hash-based algorithms and a series of refinements to the partitioning technique. Within each series, it can be proved that each method discovers at least as many redundancies as its predecessors. Unfortunately, no such relationship exists between the hash-based and global techniques. On some programs, the hash-based techniques eliminate more redundancies than the partitioning techniques, while on others, partitioning wins. We experimentally compare the improvements made by these techniques when applied to real programs. These results will be useful for commercial compiler writers who wish to assess the potential impact of each technique before implementation.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e997c2b7602d9701fb115d

This thesis presents a compilation framework for translating ANSI C programs into hardware dataflow machines. The framework is embodied in the CASH compiler, a Compiler for Application-Specific Hardware. CASH generates asynchronous hardware circuits that directly implement the functionality of the source program, without using any interpretative structures. This style of computation is dubbed “Spatial Computation.” CASH relies extensively on predication and speculation for building efficient hardware circuits. The first part of this document describes Pegasus, the internal representation of CASH, and a series of novel program transformations performed by CASH. The most notable of these are a new optimal register-promotion algorithm and partial redundancy elimination for memory accesses based on predicate manipulation. The second part of this document evaluates the performance of the generated circuits using simulation. Using media processing benchmarks, we show that for the domain of embedded computation, the circuits generated by CASH can sustain high levels of instruction level parallelism, due to the effective use of dataflow software pipelining. A comparison of Spatial Computation and superscalar processors highlights some of the weaknesses of our model of computation, such as the lack of branch prediction and register renaming. Low-level simulation however suggests that the energy efficiency of Application-Specific Hardware is three orders of magnitude better than superscalar processors, one order of magnitude better than low-power digital signal processors and asynchronous processors, and approaching custom hardware chips. The results presented in this document can be applied in several domains: (1) most of the compiler optimizations are applicable to traditional compilers for high-level languages; (2) CASH itself can be used as a hardware synthesis tool for very fast system-on-a-chip prototyping directly from C sources; (3) the compilation framework we describe can be applied to the translation of imperative languages to dataflow machines; (4) we have extended the dataflow machine model to encompass predication, data-speculation and control-speculation; and (5) the tool-chain described and some specific optimizations, such as lenient execution and pipeline balancing, can be used for synthesis and optimization of asynchronous hardware.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e997d1b7602d9701fc4fc8

Source model extraction---the automated extraction of information from system artifacts---is a common phase in reverse engineering tools. One of the major challenges of this phase is creating extractors that can deal with irregularities in the artifacts ...

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e997d6b7602d9701fc9639

Many real-time operating systems (RTOSes) offer very small interrupt latencies, in the order of tens or hundreds of cycles. They achieve this by making the RTOS kernel fully preemptible, permitting interrupts at almost any point in execution except for some small critical sections. One drawback of this approach is that it is difficult to reason about or formally model the kernel's behavior for verification, especially when written in a low-level language such as C. An alternate model for an RTOS kernel is to permit interrupts at specific preemption points only. This controls the possible interleavings and enables the use of techniques such as formal verification or model checking. Although this model cannot (yet) obtain the small interrupt latencies achievable with a fully-preemptible kernel, it can still achieve worst-case latencies in the range of 10,000s to 100,000s of cycles. As modern embedded CPUs enter the 1 GHz range, such latencies become acceptable for more applications, particularly when they come with the additional benefit of simplicity and formal models. This is particularly attractive for protected multitasking microkernels, where the (inherently non-preemptible) kernel entry and exit costs dominate the latencies of many system calls. This paper explores how to reduce the worst-case interrupt latency in a (mostly) non-preemptible protected kernel, and still maintain the ability to apply formal methods for analysis. We use the formally-verified seL4 microkernel as a case study and demonstrate that it is possible to achieve reasonable response-time guarantees. By combining short predictable interrupt latencies with formal verification, a design such as seL4's creates a compelling platform for building mixed-criticality real-time systems.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e997ddb7602d9701fd31fa

Due to limits in technology scaling, energy efficiency of logic devices is decreasing in successive generations. To provide continued performance improvements without increasing power, regardless of the sequential or parallel nature of the application, microarchitectural energy efficiency must improve. We propose Dynamically Specialized Datapaths to improve the energy efficiency of general purpose programmable processors. The key insights of this work are the following. First, applications execute in phases and these phases can be determined by creating a path-tree of basic-blocks rooted at the inner-most loop. Second, specialized datapaths corresponding to these path-trees, which we refer to as DySER blocks, can be constructed by interconnecting a set of heterogeneous computation units with a circuit-switched network. These blocks can be easily integrated with a processor pipeline. A synthesized RTL implementation using an industry 55nm technology library shows a 64-functional-unit DySER block occupies approximately the same area as a 64 KB single-ported SRAM and can execute at 2 GHz. We extend the GCC compiler to identify path-trees and code-mapping to DySER and evaluate the PAR-SEC, SPEC and Parboil benchmarks suites. Our results show that in most cases two DySER blocks can achieve the same performance (within 5%) as having a specialized hardware module for each path-tree. A 64-FU DySER block can cover 12% to 100% of the dynamically executed instruction stream. When integrated with a dual-issue out-of-order processor, two DySER blocks provide geometric mean speedup of 2.1X (1.15X to 10X), and geometric mean energy reduction of 40% (up to 70%), and 60% energy reduction if no performance improvement is required.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e997ddb7602d9701fd38c7

Predicate abstraction is a major abstraction technique for the verification of software. Data is abstracted by means of Boolean variables, which keep track of predicates over the data. In many cases, the technique suffers from the fact that it requires at least one predicate for each iteration of a loop construct in the program. We propose to extract looping counterexamples from the abstract model, and to parameterize the simulation instance in the number of loop iterations.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e997edb7602d9701fec6c1

Abstract: Array privatization is one of the most effective transformationsfor the exploitation of parallelism. In this paper, we presentatechniquefor automatic array privatization. Our algorithm uses data flowanalysis of array references to identify privatizable arrays intraprocedurallyas well as interprocedurally. It employs static and dynamic resolutionto determine the last value of a lived private array.We comparethe result of automatic array privatization with that of manual array...

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e997edb7602d9701fecdd8

The success of model checking for large programs depends crucially on the ability to efficiently construct parsimonious abstractions. A predicate abstraction is parsimonious if at each control location, it specifies only relationships between current values of variables, and only those which are required for proving correctness. Previous methods for automatically refining predicate abstractions until sufficient precision is obtained do not systematically construct parsimonious abstractions: predicates usually contain symbolic variables, and are added heuristically and often uniformly to many or all control locations at once. We use Craig interpolation to efficiently construct, from a given abstract error trace which cannot be concretized, a parsominous abstraction that removes the trace. At each location of the trace, we infer the relevant predicates as an interpolant between the two formulas that define the past and the future segment of the trace. Each interpolant is a relationship between current values of program variables, and is relevant only at that particular program location. It can be found by a linear scan of the proof of infeasibility of the trace.We develop our method for programs with arithmetic and pointer expressions, and call-by-value function calls. For function calls, Craig interpolation offers a systematic way of generating relevant predicates that contain only the local variables of the function and the values of the formal parameters when the function was called. We have extended our model checker BLAST with predicate discovery by Craig interpolation, and applied it successfully to C programs with more than 130,000 lines of code, which was not possible with approaches that build less parsimonious abstractions.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e997f5b7602d9701ff9693

An important function of any register allocator is to target registers so as to eliminate copy instructions. Graph-coloring register allocation is an elegant approach to this problem. If the source and destination of a move instruction do not interfere, then their nodes can be coalesced in the interference graph. Chaitin's coalescing heuristic could make a graph uncolorable (i.e., introduce spills); Briggs et al. demonstrated a conservative coalescing heuristic that preserves colorability. But Briggs's algorithm is too conservative and leaves too many move instructions in our programs. We show how to interleave coloring reductions with Briggs's coalescing heuristic, leading to an algorithm that is safe but much more aggressive.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e997f9b7602d9702003602

Program-dependence information is useful for a variety of applications, such as software testing and maintenance tasks, and code optimization. Properly defined, control and data dependences can be used to identify semantic dependences. To function effectively on whole programs, tools that utilize dependence information require information about interprocedural dependences: dependences that are identified by analyzing the interactions among procedures. Many techniques for computing interprocedural data dependences exist; however, virtually no attention has been paid to interprocedural control dependence. Analysis techniques that fail to account for interprocedural control dependences can suffer unnecessary imprecision and loss of safety. This article presents a definition of interprocedural control dependence that supports the relationship of control and data dependence to semantic dependence. The article presents two approaches for computing interprocedural control dependences, and empirical results pertaining to teh use of those approaches.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e997f9b7602d9702003605

A piece of code in a computer program is infeasible if it cannot be part of any normally-terminating execution of the program. We develop an algorithm for the automatic detection of all infeasible code in a program. We first translate the task of determining all infeasible code into the problem of finding all statements that can be covered by a feasible path. We prove that in order to identify all coverable statements, it is sufficient to find all coverable statements within a certain minimal subset. For this, our algorithm repeatedly queries an oracle, asking for the infeasibility of specific sets of control-flow paths. We present a sound implementation of the proposed algorithm on top of the Boogie program verifier utilizing a theorem prover to provide the oracle required by the algorithm. We show experimentally a drastic decrease in the number of theorem prover queries compared to existing approaches, resulting in an overall speedup of the entire computation.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e997fcb7602d9702003ead

We consider the state of the art in compiler construction and where to g from here. Main topics are improved exploitation of present (and future) hardware features, the interaction between compiling techniques and processor design, and the use of compiling techniques in application areas such as component-based software engineering and software reengineering.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e997fcb7602d97020068f3

Erlang is a concurrent functional language designed for developing large-scale, distributed, fault-tolerant systems. The primary implementation of the language is the Erlang/OTP system from Ericsson. Even though Erlang/OTP is by default based on a virtual machine interpreter, it nowadays also includes the HiPE (High Performance Erlang) native code compiler as a fully integrated component. This paper describes the recently developed port of HiPE to the AMD64 architecture. We discuss technical issues that had to be addressed when developing the port, decisions we took and why, and report on the speedups (compared with BEAM) which HiPE/AMD64 achieves across a range of Erlang programs and how these compare with speedups for the more mature SPARC and x86 back-ends.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99813b7602d9702028ed9

Any programming error that can be revealed before compiling a program saves precious time for the programmer. While integrated development environments already do a good job by detecting, e.g., data-flow abnormalities, current static analysis tools suffer from false positives ("noise") or require strong user interaction.We propose to avoid this deficiency by defining a new class of errors. A program fragment is doomed if its execution will inevitably fail, regardless of which state it is started in. We use a formal verification method to identify such errors fully automatically and, most significantly, without producing noise. We report on experiments with a prototype tool.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e9981db7602d970203e2f4

The static single information (SSI) form is an extension of the static single assignment (SSA) form, a well-established compiler intermediate representation that has been successfully used for numerous compiler analysis and optimizations. Several interesting results have also been shown for SSI form concerning liveness analysis and the representation of live-ranges of variables, which could make SSI form appealing for just-in-time compilation. Unfortunately, we have uncovered several mistakes in the previous literature on SSI form, which, admittedly, is already quite sparse. This article corrects the mistakes that are most germane to SSI form. We first explain why the two definitions of SSI form proposed in past literature, first by C. S. Ananian, then by J. Singer, are not equivalent. Our main result is then to prove that basic blocks, and thus program points, can be totally ordered so that live-ranges of variables correspond to intervals on a line, a result that holds for both variants of SSI form. In other words, in SSI form, the intersection graph defined by live-ranges is an interval graph, a stronger structural property than for SSA form for which the intersection graph of live-ranges is chordal. Finally, we show how this structure of live-ranges can be used to simplify liveness analysis.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99821b7602d9702040673

Many analyses and transformations in a parallelizing compiler can benefit from the ability to compare arbitrary symbolic expressions. In this paper, we describe how one can compare expressions by using symbolic ranges of variables. A range is a lower and upper bound on a variable. We describe how these ranges can be efficiently computed from the program test. Symbolic range propagation has been implemented in Polaris, a parallelizing compiler being developed at the University of Illinois, and is used for symbolic dependence testing, detection of zero-trip loops, determining array sections possibly referenced by an access, and loop iteration-count estimation.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99827b7602d970204afde

Recent results on the static single assignment (SSA) form open promising directions for the design of register allocation heuristics for just-in-time (JIT) compilation. In particular, tree-scan allocators with two decoupled phases, one for spilling and one for splitting/coloring/coalescing, seem good candidates for designing fast, memory-friendly, and competitive register allocators. Linear-scan allocators, introduced earlier, are also well-suited for JIT compilation. All do live-range splitting (mostly on control-flow edges) to avoid spilling but most of them perform coalescing poorly, leading to many register-to-register copies inside basic blocks, but also, implicitly, on the control-flow graph edges, leading to edge splitting. This paper presents parallel copy motion, a technique for optimizing register-allocated codes, which amounts to moving a group of parallel copy instructions from a program point to another. While the scheduling is shackled by data dependencies, a copy can "traverse" all instructions of a basic block, thanks to register renaming, except those with conflicting naming constraints. Also, with an adequate management of compensation code, parallel copies can also be moved across edges. A first application is reducing the cost of copies by a better placement. A second application is moving copies out of critical edges, i.e., edges going from a block with multiple successors to a block with multiple predecessors. This is often beneficial compared to the alternative: splitting the edge. A direct use case is the handling of control-flow graphs with non-splittable edges, introduced by some compilers for specific architectural constraints, region boundaries, or exception handling code. Experiments with the SPECint and our own benchmarks suite show that an SSA-based register allocator can be applied broadly now, even for procedures with non-splittable edges: while those procedures could not be compiled before, with parallel copy motion, all moves could be pushed out of such edges. Even simple strategies for moving copies out of edges and inside basic blocks show some average improvement compared to the standard edge-splitting strategy (3% speedup), with a great reduction of the weighted number of copies (21% move cost reduction for SPECint). This lets us believe that the approach is promising, and not only for improving coalescing in fast register allocators.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e9982cb7602d9702052ee9

Program slicing is a technique for isolating computational threads in programs. In this paper, we show how to mechanically extract a family of practical algorithms for computing slices directly from semantic specifications. These algorithms are based on combining the notion of dynamic dependence tracking in term rewriting systems with a program representation whose behavior is defined via an equational logic. Our approach is distinguished by the fact that changes to the behavior of the slicing algorithm can be accomplished through simple changes in rewriting rules that define the semantics of the program representation. Thus, e.g., different notions of dependence may be specified, properties of language-specific datatypes can be exploited, and various time, space, and precision tradeoffs may be made. This flexibility enables us to generalize the traditional notions of static and dynamic slices to that of a constrained slice, where any subset of the inputs of a program may be supplied.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99838b7602d970206253e

Operator strength reduction is a technique that improves compiler-generated code by reformulating certain costly computations in terms of less expensive ones. A common case arises in array addressing expressions used in loops. The compiler can replace the sequence of multiplies generated by a direct translation of the address expression with an equivalent sequence of additions. When combined with linear function test replacement, strength reduction can speed up the execution of loops containing array references. The improvement comes from two sources: a reduction in the number of operations needed to implement the loop and the use of less costly operations.This paper presents a new algorithm for operator strength reduction, called OSR. OSR improves upon an earlier algorithm of Allen, Cocke, and Kennedy [Allen et al. 1981]. OSR operates on the static single assignment (SSA) form of a procedure [Cytron et al. 1991]. By taking advantage of the properties of SSA form, we have derived an algorithm that is simple to understand, quick to implement, and, in practice, fast to run. Its asymptotic complexity is, in the worst case, the same as the Allen, Cocke,and Kennedy algorithm (ACK). OSR achieves optimization results that are equivalent to those obtained with the ACK algorithm. OSR has been implemented in several research and production compilers.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e9983db7602d9702063893

Graph-coloring register allocators eliminate copiesby coalescing the source and target node of a copy ifthey do not interfere in the interference graph. Coalescingis, however, known to be harmful to the colorabilityof the graph because it tends to yield a graphwith nodes of higher degrees. Unlike aggressive coalescingwhich coalesces any pair of non-interfering copyrelatednodes, conservative coalescing or iterated coalescingperform safe coalescing that preserves the colorability....

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99842b7602d97020712e8

Memory expansions are classical means to extract parallelismfrom imperative programs. However, for dynamiccontrol programs with general memory accesses, such transformationseither fail or require some run-time mechanismto restore the data flow. This paper presents an expansionframework for any type of data structure in any imperativeprogram, without the need for dynamic data flow restoration.The key idea is to group together the write operationsthat participate in the flow of the same...

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99842b7602d97020712ea

Memory expansions are classical means to extract parallelism from imperative programs. However, current techniques require some runtime mechanism to restore data flow when expansion maps have two definitions reaching the same use to two different memory locations (e.g., &phis; functions in the SSA framework). This paper presents an expansion framework for any type of data structure in any imperative program, without the need for dynamic data flow restoration. The key idea is to group together definitions that reach a common use. We show that such an expansion boils down to mapping each group to a memory cell.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e9984bb7602d9702079084

Any program that measures quantities from its physical environment must compute using correct and consistent units of measurement. Such a program is described here as well-measuring. In many systems, particularly embedded control software, paying inadequate attention to units of measurement can result in catastrophe. Unfortunately, current programming languages and tools provide little aid to the programmer attempting to establish or verify the wellmeasuring property. We present a program analysis technique for inferring and checking the units used within a program. The technique combines traditional Hindley-Milner-style type inference with the use of Static Single Assignment (SSA) form to enable analysis of imperative programs.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e9984fb7602d97020827e7

Silicon technology will continue to provide an exponential increase in the availability of raw transistors. Effectively translating this resource into application performance, however, is an open challenge that conventional superscalar designs will not be able to meet. We present WaveScalar as a scalable alternative to conventional designs. WaveScalar is a dataflow instruction set and execution model designed for scalable, low-complexity, high-performance processors. Unlike previous dataflow machines, WaveScalar can efficiently provide the sequential memory semantics imperative languages require. To allow programmers to easily express parallelism, WaveScalar supports pthread-style, coarse-grain multithreading and dataflow-style, fine-grain threading. In addition, it permits blending the two styles within an application or even a single function. To execute WaveScalar programs, we have designed a scalable, tile-based processor architecture called the WaveCache. As a program executes, the WaveCache maps the program's instructions onto its array of processing elements (PEs). The instructions remain at their processing elements for many invocations, and as the working set of instructions changes, the WaveCache removes unused instructions and maps new instructions in their place. The instructions communicate directly with one-another over a scalable, hierarchical on-chip interconnect, obviating the need for long wires and broadcast communication. This thesis presents the WaveScalar instruction set and evaluates a simulated implementation based on current technology. For single-threaded applications, the WaveCache achieves performance on par with conventional processors, but in less area. For coarse-grain threaded applications, WaveCache performance scales with chip size over a wide range, and it outperforms a range of the multi-threaded designs. The WaveCache sustains 7-14 multiply-accumulates per cycle on fine-grain threaded versions of well-known kernels. Finally, we apply both styles of threading to an example application, equake from spec2000, and speed it up by 9× compared to the serial version.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e9984fb7602d9702083f61

This paper presents tree register allocation, which maps the lifetimes of the variables in a program into a set of trees, colors each tree in a greedy style, which is optimal when there is no spilling, and connects dataflow between and within the trees afterward. This approach generalizes and subsumes as special cases SSA-based, linear scan, and local register allocation. It keeps their simplicity and low throughput cost, and exposes a wide solution space beyond them. Its flexibility enables control flow structure and/or profile information to be better reflected in the trees. This approach has been prototyped in the Phoenix production compiler framework. Preliminary experiments suggest this is a promising direction with great potential. Register allocation based on two special kinds of trees, extended basic blocks and the maximal spanning tree, are found to be competitive alternatives to SSA-based register allocation, and they all tend to generate better code than linear scan.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99859b7602d9702095cba

The complexity of round robin iterative data flow analysis has been traditionally defined as 1 + d where d is the depth of a control flow graph. However, this bound is restricted to bit vector frameworks, which by definition, are separable. For non-separable frame- works, the complexity of analysis is influenced by the interdependences of program entities, hence the bound of 1 + d is not applicable. This motivates the need for capturing the interdependences of entities to define a general complexity measure. We propose Degree of dependence δ which quanti- fies the effect of non-separability on the complexity of analysis for a particular problem instance. We define the complexity bound of 1 + δ + d which explains the complexity of round robin analysis of general non- separable data flow problems. Like d , δ is a theoretical concept useful for understanding the complexity rather than estimating it. In bit vector frameworks the bound 1 + δ + d reduces to 1 + d due to δ = 0. Apart from being general, our bound is also precise, as corroborated by empirical results.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99893b7602d97020c9a83

We consider problems related to dominators and independent spanning trees in flowgraphs and provide linear-time algorithms for their solutions. We introduce the notion of a directed bipolar order, generalizing a previous notion of Plein and Cheriyan and Reif. We show how to construct such an order from information computed by several known algorithms for finding dominators. We show how to concurrently verify the correctness of a dominator tree D and a directed bipolar order O very simply, and how to construct from D and O two spanning trees whose paths are disjoint except for common dominators. Finally, we describe alternative ways to verify dominators without using a directed bipolar order.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99894b7602d97020cf35b

This paper proposes a new FPGA-based embedded computer architecture, which focuses on how to construct an application-specific memory access network capable of extracting the maximum amount of memory-level parallelism on a per-application basis. Specifically, through performing dynamic memory analysis and utilizing the capabilities of modern FPGA devices: abundant distributed block RAMs and programmability, the proposed reconfigurable architecture synthesizes highly efficient accelerators that enable parallelized memory accesses, and therefore accomplish effective data orchestration by maximally extracting the target application's instruction, loop and memory-level parallelism. To validate our proposed architecture, we implemented a baseline embedded processor platform, a conventional CPU +accelerator with a centralized single memory, and a prototype based on Xilinx MicroBlaze technology. Our experimental results have shown that on average for 5 benchmark applications from SPEC2006 and MiBench [1], our proposed architecture achieves 8.6 times speedup compared to the baseline embedded processor platform and 1.7 times speedup compared to a conventional CPU+accelcrator platform. More interestingly, the proposed platform achieves more than 40% reduction in energy-delay product compared to a conventional CPU+accelerator with a centralized memory.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e998a9b7602d97020e29ea

We introduce the concept of future values. Using future values it is possible to represent programs in a new control-flow form such that on any control flow path the data-flow aspect of the computation is either traditional (i.e., definition of a value precedes its consumers), or reversed (i.e., consumers of a value precede its definition). The representation hence allows unrestricted code motion since ordering of instructions are not prohibited by the data dependencies. We present a new program representation called Recursive Future Predicated Form (RFPF) which implements the concept. RFPF subsumes general if-conversion and permits unrestricted code motion to the extent that the whole procedure can be reduced to a single block. We develop algorithms which enable instruction movement in acyclic as well as cyclic regions and give examples of various optimizations in RFPF form.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e998b8b7602d97020f44f9

Automatic object inlining [19, 20] transforms heapdata structures by fusing parent and child objects together. It canimprove runtime by reducing object allocation and pointer dereferencecosts. We report continuing work studying object inlining optimizations. In particular, we present a new semantic derivation of the correctness conditions for object inlining, and program analysis which extends our previous work. And we present an object inlining transformation, focusing on a new algorithm which optimizes class field layout to minimize code expansion. Finally, we detail a fuller evaluation on eleven programs and libraries (including Xpdf, the 25,000 line Portable Document Format (PDF) file browser) that utilizes hardware measures of impact on the memory system. We show that our analysis scales effectively to large programs,finding many inlinable fields (45 in xpdf) at acceptable cost, and weshow that, on some programs, it finds nearly all fields for which object inlining is correct, and averages 40% of such fields across our benchmarks. We implement our analyses in an advanced analysis infrastructure, and we show that, compared to traditional 1-CFA, that infrastructure provides better results and lower and more scalable cost. Across all programs, analysis identified about 30% of objects as inlinable on average. Our transformation increases code size by only 20% while inlining this 30% of fields. Inlining these objects eliminated on average 28% of field reads, 58% of object creations, 12% of all loads. Further, the optimized programs have significantly improved memory reference behavior, producing 25% fewer L1 data cache misses and 25% fewer read stalls. On average the runtime improved by 14%.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e998bfb7602d97020fa732

The available static analyzers of run-time errors cannot find the most part of real errors and are able to generate only long lists of warnings. In this paper, we determine the conditions of a definite error and propose the list of program analyses imposed by these conditions. The static error checker OSA based on effective algorithm of powerful context-sensitive data flow analysis with the approximation of definite use-def relations is developed.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e998c7b7602d97020fd59d

We present a new static single assignment form which can be used by an optimizing compiler as its internal representation and the micro-architecture as its instruction set. This representation, Future Gated Single Assignment Form (FGSA), directly represents the use-def relationship of variables by employing the concept of congruence classes and the concept of future dependencies. We show that FGSA is efficiently computable by using a series of T1/T2 transformations, yielding an expected linear time algorithm for the construction of single assignment form. Our interval analysis method includes a novel transformation TR which eliminates irreducible loops without node splitting and combines computation of single-assignment form with irreducible loop elimination. The algorithm produces pruned single assignment form, rendering a separate pruning step unnecessary. In practice, the FGSA approach results in an average reduction of 7.7%, with a maximum of 67% in the number of gating functions compared to the pruned SSA form on the SPEC2000 benchmark suite, owing to its ability to represent dataflow within a congruence class by using a single gating function. We illustrate that FGSA is convenient to use as an internal representation in an optimizing compiler by presenting two case studies of optimization algorithms on FGSA.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e998ceb7602d970210acc0

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e998dbb7602d970211a1f3

Optionally typed languages enable direct performance comparisons between untyped and type annotated source code. We present a comprehensive performance evaluation of two different JIT compilers in the context of ActionScript, a production-quality optionally typed language. One JIT compiler is optimized for quick compilation rather than JIT compiled code performance. The second JIT compiler is a more aggressively optimizing compiler, performing both high-level and low-level optimizations. We evaluate both JIT compilers directly on the same benchmark suite, measuring their performance changes across fully typed, partially typed, and untyped code. Such evaluations are especially relevant to dynamically typed languages such as JavaScript, which are currently evaluating the idea of adding optional type annotations. We demonstrate that low-level optimizations rarely accelerate the program enough to pay back the investment into performing them in an optionally typed language. Our experiments and data demonstrate that high-level optimizations are required to improve performance by any significant amount.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e998f6b7602d97021307d5

The ability to predict at compile time the likelihood of a particular branch being taken provides valuable information for several optimizations, including global instruction scheduling, code layout, function inlining, interprocedural register allocation and many high level optimizations. Previous attempts at static branch prediction have either used simple heuristics, which can be quite inaccurate, or put the burden onto the programmer by using execution profiling data or source code hints.This paper presents a new approach to static branch prediction called value range propagation. This method tracks the weighted value ranges of variables through a program, much like constant propagation. These value ranges may be either numeric of symbolic in nature. Branch prediction is then performed by simply consulting the value range of the appropriate variable. Heuristics are used as a fallback for cases where the value range of the variable cannot be determined statically. In the process, value range propagationsubsumes both constant propagation and copy propagation.Experimental results indicate that this approach produces significantly more accurate predictions than the best existing heuristic techniques. The value range propagation method can be implemented over any “factored” dataflow representation with a static single assignment property (such as SSA form or a dependence flow graph where the variables have been renamed to achieve single assignment). Experimental results indicate that the technique maintains the linear runtime behavior of constant propagation experienced in practice.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99905b7602d97021448bf

We introduce an automated parameterized verification method for fault-tolerant distributed algorithms (FTDA). FTDAs are parameterized by both the number of processes and the assumed maximum number of Byzantine faulty processes. At the center of our technique is a parametric interval abstraction (PIA) where the interval boundaries are arithmetic expressions over parameters. Using PIA for both data abstraction and a new form of counter abstraction, we reduce the parameterized problem to finite-state model checking. We demonstrate the practical feasibility of our method by verifying several variants of the well-known distributed algorithm by Srikanth and Toueg. Our semi-decision procedures are complemented and motivated by an undecidability proof for FTDA verification which holds even in the absence of interprocess communication. To the best of our knowledge, this is the first paper to achieve parameterized automated verification of Byzantine FTDA.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e9990cb7602d9702146379

Static single assignment (SSA) form has been widely, studied and used for sequential programs. This form enables many compiler optimizations to be done efficiently,. Work on concurrent static single assignment form (CSSA) for concurrent programs is focused on languages that have limited, implicit barriers (e.g., cobegin/coend and parallel do). Recent programming languages for high-performance computing have general features for barrier/phase synchronization - this is essentially a dual of mutual exclusion and arises mainly in constructing,synchronous systems from asynchronous systems. X10 is one such language that has features for general purpose barriers. In X10, barriers are provided through features such as clocks and finish. Since barriers provide explicit synchronization, they offer an opportunity for reducing pi interferences needed for CSSA. This paper provides a means for computing improved CSSA form of a program taking advantage of the general barriers present in it. Our algorithm. is based on constructing a control-flow, graph of the program and flow equations. The efficiency of analysis and optimizations for parallel programs depends on the number and complexity of pi assignments in their CSSA representations. We demonstrate that our approach of computing CSSA form for languages supporting general barrier synchronization can improve the precision of intermediate representation for computing global value numbering and loop invariant detection.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e9991cb7602d97021577a4

Computational scientists often must choose between the greater programming productivity of high-level abstractions, such as matrices and mesh entities, and the greater execution efficiency of low-level constructs. Performance is degraded when abstraction indirection introduces overhead and hinders compiler analysis. This can be overcome by targeting the semantics, rather than the implementation, of abstractions. Raising operators specified by a domain expert project an application from an implementation space to an abstraction space, where optimizations leverage domain semantics to complement conservative analyses. Raising operators define a domain-specific intermediate representation, which optimizations target for improved portability. Following optimization, transformed code is reified as a concrete implementation via lowering operators. We have developed a framework to implement this optimization strategy, which we use to introduce two domain-specific unstructured mesh optimizations. The first uses an inspector/executor approach to avoid costly traversals over a static mesh by memoizing the relatively few references required for mathematical computations. The executor phase accesses stored entities without incurring the indirections. The second optimization lowers object-based mesh access and iteration to a low-level implementation, which uses integer-based access and iteration.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99924b7602d970215c262

Concurrency analysis is a static analysis technique that determines whether two statements or operations in a shared memory program may be executed by different threads concurrently. Concurrency relationships can be derived from the partial ordering among statements imposed by synchronization constructs. Thus, analyzing barrier synchronization is at the core of concurrency analyses for many parallel programming models. Previous concurrency analyses for programs with barriers commonly assumed that barriers are named or textually aligned. This assumption may not hold for popular parallel programming models, such as OpenMP, where barriers are unnamed and can be placed anywhere in a parallel region, i.e., they may be textually unaligned. We present in this paper the first interprocedural concurrency analysis that can handle OpenMP, and, in general, programs with unnamed and textually unaligned barriers.We have implemented our analysis for OpenMP programs written in C and have evaluated the analysis on programs from the NPB and SpecOMP2001 benchmark suites.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99931b7602d970216d297

Loop nesting forests and dominator trees are important tools in program optimization and code generation, and they have applications in other diverse areas. In this work we first present carefully engineered implementations of efficient algorithms for computing a loop nesting forest of a given directed graph, including a very efficient algorithm that computes the forest in a single depth-first search. Then we revisit the problem of computing dominators and present efficient implementations of the algorithms recently proposed by Fraczak et al. [12], which include an algorithm for acyclic graphs and an algorithm that computes both the dominator tree and a loop nesting forest. We also propose a new algorithm than combines the algorithm of Fraczak et al. for acyclic graphs with the algorithm of Lengauer and Tarjan. Finally, we provide fast algorithms for the following related problems: computing bridges and testing 2-edge connectivity, verifying dominators and testing 2-vertex connectivity, and computing a low-high order and two independent spanning trees. We exhibit the efficiency of our algorithms experimentally on large graphs taken from a variety of application areas.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99931b7602d970216cdf6

. When parallelizing an imperative program, a key problem is to find the good tradeoff betweenmemory expansion and parallelism. Increasing performance of parallelizing compilers thus relieson a difficult multi-criteria optimization problem. This paper is a first step in solving this problem: Anintegrated framework for parallel execution order and storage mapping computation is designed, allowingsimultaneous time and space optimization of parallel programs. The use of constrained...

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99938b7602d97021795de

In this paper, we propose an automated technique for optimal instrumentation of multi-threaded programs for debugging and testing of concurrent data structures. We define a notion of observability that enables debuggers to trace back and locate errors through data-flow instrumentation. Observability in a concurrent program enables a debugger to extract the value of a set of desired variables through instrumenting another (possibly smaller) set of variables. We formulate an optimization problem that aims at minimizing the size of the latter set. In order to cope with the exponential complexity of the problem, we present a SAT-based solution. Our approach is fully implemented and experimental results on popular concurrent data structures (e.g., linked lists and red-black trees) show significant performance improvement in optimally-instrumented programs using our method as compared to ad-hoc over-instrumented programs.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99946b7602d9702185aad

Dependence analysis algorithms have been proposed to identify parallelism in programs with tree-like data structures. However, they cannot analyze the dependence of statements if recursive data structures of programs are cyclic. This paper presents a technique to identify parallelism in programs with cyclic graphs. The technique consists of three steps: (1) traversal patterns that loops or recursive procedures traverse graphs are identified, and the statements that construct the links of traversal patterns will be located by definition-use chains of recursive data structures; (2) traversal-pattern-sensitive shape analysis is performed to estimate possible shapes of traversal patterns, (3) dependence analysis is performed to identify parallelism using the result of shape analysis. This approach can identify parallelism in programs with cyclic data structures due to the facts that many programs follow acyclic structures (i.e. traversal patterns) to access all nodes on the cyclic data structures. Once the traversal patterns are isolated from the overall data structures, dependence analysis can be applied to identify parallelism.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e9994db7602d970218f1fa

In recent work we have proposed a novel approach to define idealized type systems for object-oriented languages, based on abstract compilation of programs into Horn formulas which are interpreted w.r.t. the coinductive (that is, the greatest) Herbrand model. In this paper we investigate how this approach can be applied also in the presence of imperative features. This is made possible by considering a natural translation of Static Single Assignment intermediate form programs into Horn formulas, where. functions correspond to union types.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e9998bb7602d97021cf209

In this paper we propose a hardware assisted software protection scheme that relies on the use of a resource-limited secure token ({\\em e.g.} a smart card). The protection consists in externalizing the execution of the sensitive pieces of code of the application to be protected to the token block by block, while the unsensitive code is still executed inside the untrusted computer. We define a generic process: the protection is enforced automatically. Our method relies on static analysis techniques that are used to infer the parts of code to be externalized together with run-time externalization protocol. We have developed a software environment implementing this technology for Java applications.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99998b7602d97021dde09

Load-reuse analysis finds instructions that repeatedly access the same memory location. This location can be promoted to a register, eliminating redundant loads by reusing the results of prior memory accesses. This paper develops a load-reuse analysis and designs a method for evaluating its precision.In designing the analysis, we aspire for completeness---the goal of exposing all reuse that can be harvested by a subsequent program transformation. For register promotion, a suitable transformation is partial redundancy elimination (PRE). To approach the ideal goal of PRE-completeness, the load-reuse analysis is phrased as a data-flow problem on a program representation that is path-sensitive, as it detects reuse even when it originates in a different instruction along each control flow path. Furthermore, the analysis is comprehensive, as it treats scalar, array and pointer-based loads uniformly.In evaluating the analysis, we compare it with an ideal analysis. By observing the run-time stream of memory references, we collect all PRE-exploitable reuse and treat it as the ideal analysis performance. To compare the (static) load-reuse analysis with the (dynamic) ideal reuse, we use an estimator algorithm that computes, given a data-flow solution and a program profile, the dynamic amount of reuse detected by the analysis. We developed a family of estimators that differ in how well they bound the profiling error inherent in the edge profile. By bounding the error, the estimators offer a precise and practical method for determining the run-time optimization benefit.Our experiments show that about 55% of loads executed in Spec95 exhibit reuse. Of those, our analysis exposes about 80%.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e999b4b7602d97021f8430

The GCC (GNU Compiler Collection) project of the Free Software Foundation has resulted in one of the most widespread compilers in use today that is capable of generating code for a variety of platforms. Since 1987, many volunteers from academia and the private sector have been working to continuously improve the functionality and quality of GCC. Some of the compiler's key components were, and continue to be, developed at IBM Research laboratories. We review several of IBM's contributions to the compiler, including a code generator for the IBM zSeries® processor and a front end for a PL/I-like language used for systems software programming. We also cover many optimizations, including the interblock instruction scheduler, software pipeliner, and vectorizer. These contributions help improve the overall performance of code generated by GCC, and in particular, enhance the IBM RISC (reduced instruction set computer) architecture and the zSeries processors. This paper includes a report on our general experience with GCC in both open source and proprietary software environments and reviews the quality and performance of GCC-generated code.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e999b5b7602d97021fc4bb

In this paper we develop compilation techniques for the realization of applications described in a High Level Language (HLL) onto a Runtime Reconfigurable Architecture. The compiler determines Hyper Operations (HyperOps) that are subgraphs of a data flow graph (of an application) and comprise elementary operations that have strong producer-consumer relationship. These HyperOps are hosted on computation structures that are provisioned on demand at runtime. We also report compiler optimizations that collectively reduce the overheads of data-driven computations in runtime reconfigurable architectures. On an average, HyperOps offer a 44% reduction in total execution time and a 18% reduction in management overheads as compared to using basic blocks as coarse grained operations. We show that HyperOps formed using our compiler are suitable to support data flow software pipelining.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e999c3b7602d9702205bed

Program inversion has been successfully applied to several areas such as optimistic parallel discrete event simulation (OPDES) and reverse debugging. This paper introduces a new program inversion algorithm for imperative languages, and focuses on handling arbitrary control flows and basic operations. By building a value search graph that represents recoverability relationships between variable values, we turn the problem of recovering previous values into a graph search one. Forward and reverse code is generated according to the search results. We have implemented our algorithm as part of a compiler framework named Backstroke, a C++ source-to-source translator based on ROSE compiler. Backstroke targets optimistic simulation codes and automatically generates a reverse function to recover values modified by a target function. Experimental results show that our method is effective and produces better performance than previously proposed methods.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e999e0b7602d9702224360

Conventional compilers are designed for producing highly optimized code without paying much attention to compile time. The design goals of Java just-in-time compil- ers are different: produce fast code at the smallest possible compile time. In this article we present a very fast algorithm for translating JavaVM byte code to high quality machine code for RISC processors. This algorithm handles combines instructions, does copy elimination and coalescing and does register allocation. It comprises three passes: basic block determination, stack analysis and register preallocation, fi- nal register allocation and machine code generation. This algorithm replaces an older one in the CACAO JavaVM im- plementation reducing the compile time by a factor of seven and producing slightly faster machine code. The speedup comes mainly from following simplifications: fixed assign- ment of registers at basic block boundaries, simple register allocator, better exception handling, better memory man- agement and fine tuning the implementation. The CACAO system is currently faster than every JavaVM implementa- tion for the Alpha processor and generates machine code for all used methods of the javac compiler and its libraries in 60 milliseconds on an Alpha workstation.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e999f5b7602d9702239f68

Whenever an array element is accessed, Java virtual machines execute a compare instruction to ensure that the index value is within the valid bounds. This reduces the execution speed of Java programs. Array bounds check elimination identifies situations in which such checks are redundant and can be removed. We present an array bounds check elimination algorithm for the Java HotSpot(TM) VM based on static analysis in the just-in-time compiler. The algorithm works on an intermediate representation in static single assignment form and maintains conditions for index expressions. It fully removes bounds checks if it can be proven that they never fail. Whenever possible, it moves bounds checks out of loops. The static number of checks remains the same, but a check inside a loop is likely to be executed more often. If such a check fails, the executing program falls back to interpreted mode, avoiding the problem that an exception is thrown at the wrong place. The evaluation shows a speedup near to the theoretical maximum for the scientific SciMark benchmark suite and also significant improvements for some Java Grande benchmarks. The algorithm slightly increases the execution speed for the SPECjvm98 benchmark suite. The evaluation of the DaCapo benchmarks shows that array bounds checks do not have a significant impact on the performance of object-oriented applications.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99a04b7602d970224ef0e

Program analysis and optimization can be speeded up through the use of the dependence flow graph (DFG), a representation of program dependences which generalizes def-use chains and static single assignment (SSA) form. In this paper, we give a simple graph-theoretic description of the DFG and show how the DFG for a program can be constructed in O(EV) time. We then show how forward and backward dataflow analyses can be performed efficiently on the DFG, using constant propagation and elimination of partial redundancies as examples. These analyses can be framed as solutions of dataflow equations in the DFG. Our construction algorithm is of independent interest because it can be used to construct a program's control dependence graph in O(E) time and its SSA representation in O(EV) time, which are improvements over existing algorithms.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99a04b7602d97022505b5

We consider the problem of dynamically maintaining a dominator tree of a flow graph, through a sequence of arc updates (insertions and deletions). We propose a new algorithm for this problem, which is based on the static dominators algorithm of Lengauer and Tarjan. Although the worst case complexity of a single update matches the running time of Lengauer-Tarjan, we exhibit the efficiency of our algorithm in practice.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99a04b7602d9702251e3f

The maintenance and evolution of critical software with high requirements for reliability is an extremely demanding, time consuming and expensive task. Errors introduced by ad-hoc changes might have disastrous effects on the system and must be prevented under all circumstances, which requires the understanding of the details of source code and system design. This paper describes Bauhaus, a comprehensive tool suite that supports program understanding and reverse engineering on all layers of abstraction, from source code to architecture.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99a04b7602d9702252e9c

The growing interest in GPU programming has brought renewed attention to the Single Instruction Multiple Data (SIMD) execution model. SIMD machines give application developers a tremendous computational power, however, the model also brings restrictions. In particular, processing elements (PEs) execute in lock-step, and may lose performance due to divergences caused by conditional branches. In face of divergences, some PEs execute, while others wait, this alternation ending when they reach a synchronization point. In this paper we introduce divergence analysis, a static analysis that determines which program variables will have the same values for every PE. This analysis is useful in three different ways: it improves the translation of SIMD code to non-SIMD CPUs, it helps developers to manually improve their SIMD applications, and it also guides the compiler in the optimization of SIMD programs. We demonstrate this last point by introducing branch fusion, a new compiler optimization that identifies, via a gene sequencing algorithm, chains of similarities between divergent program paths, and weaves these paths together as much as possible. Our implementation has been accepted in the Ocelot open-source CUDA compiler, and is publicly available. We have tested it on many industrial-strength GPU benchmarks, including Rodinia and the Nvidia's SDK. Our divergence analysis has a 34% false-positive rate, compared to the results of a dynamic profiler. Our automatic optimization adds a 3% speed-up onto parallel quick sort, a heavily optimized benchmark. Our manual optimizations extend this number to over 10%.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99a04b7602d9702254517

We describe a novel approach to performing data dependence analysis for Java in the presence of Java's "non-traditional" language features such as exceptions, synchronization, and memory consistency. We introduce new classes of edges in a dependence graph to model code motion constraints arising from these language features. We present a linear-time algorithm for constructing this augmented dependence graph for an extended basic block.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99a0ab7602d970225680d

Single-assignment and functional languages have value semantics that do not permit side-effects. This lack of side-effects makes automatic detection of parallelism and optimization for data locality in programs much easier. However, the same property poses a challenge in implementing these languages efficiently. This paper describes an optimizing compiler system that solves the key problem of aggregate copy elimination. The methods developed rely exclusively on compile-time algorithms, including interprocedural analysis, that are applied to an intermediate data flow representation. By dividing the problem into update-in-place and build-in-place analysis, a small set of relatively simple techniques edge substitution, graph pattern matching, substructure sharing and substructure targeting-was found to be very powerful. If combined properly and implemented carefully, the algorithms eliminate unnecessary copy operations to a very high degree. No run-time overhead is imposed on the compiled programs.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99a0fb7602d97022638a3

In this paper we describe ASH, an architectural framework for imple- menting Application-Specific Hardware. ASH is based on automatic hardware synthesis from high-level languages. The generated circuits use only localized computation structures; in consequence, we expect these circuits to be fast, to use little power and to scale well with program complexity. We present in detail CASH, a scalable compiler framework for ASH, which generates hardware from programs written in C. Our compiler exploits instruction level parallelism by using aggressive speculation and dynamic scheduling. Based on this compilation scheme, we evaluate the computational resources necessary for implementing complex integer-based programs, and we suggest architectural features that would support the ASH framework.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99a1ab7602d970226c4bc

Secure computation has high computational resource requirements during run-time. Secure computation optimization can lower these requirements, but has high computational resource requirements during compile-time. This prevents automatic optimization of most larger secure computations. In this paper we present an efficient optimization algorithm that does no longer require the use of a theorem prover. For a secure computation with m statements of which n are branching statements we lower the complexity from O(2^(2^n) m) to O(m^5 2^n). Using an implementation of our algorithm we can extend automatic optimization to further examples such as the AES key schedule.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99a20b7602d9702276f21

The computation of dominators in a flowgraph has applications in sev- eral areas, including program optimization, circuit testing, and theoretical biology. Lengauer and Tarjan (30) proposed two versions of a fast algo- rithm for finding dominators and compared them experimentally with an iterative bit-vector algorithm. They concluded that both versions of their algorithm were much faster even on graphs of moderate size. Recently Cooper et al. (11) have proposed a new, simple, tree-based implementa- tion of an iterative algorithm. Their experiments suggested that it was faster than the simple version of the Lengauer-Tarjan algorithm on graphs representing computer program control flows. Motivated by the work of Cooper et al., we present an experimental study comparing their algo- rithm (and some variants) with careful implementations of both versions of the Lengauer-Tarjan algorithm and with a new hybrid algorithm. Our results suggest that, although the performance of all the algorithms is sim- ilar, the most consistently fast are the simple Lengauer-Tarjan algorithm and the hybrid algorithm, and their advantage increases as the graph gets bigger or more complicated.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99a26b7602d970227d2f8

The register allocation phase of a compiler maps live ranges of a program to registers. If there are more candidates than there are physical registers, the register allocator must spill a live range (the home location is in memory) or split a live range (the live range occupies multiple locations). One of the challenges for a register allocator is to deal with spilling and splitting together. Fusion-based register allocation uses the structure of the program to make splitting and spilling decisions, with the goal to move overhead operations to infrequently executed parts of a program. The basic idea of fusion-based register allocation is to build up the interference graph. Starting with some base region (e.g., a basic block, a loop), the register allocator adds basic blocks to the region and incrementallly builds the interference graph. When there are more live ranges than registers, the register allocator selects live ranges to split; these live ranges are split along the edge that was most recently added to the region. This article describes fusion-based register allocation in detail and compares it with other approaches to register allocation. For programs from the SPEC92 suite, fusion-based register allocation can improve the execution time (of optimized programs, for the MIPS architecture) by up to 8.4% over Chaitin-style register allocation.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99a26b7602d970227d5d6

The control dependence relation is used extensively in restructuring compilers. This relation is usually represented using the control dependence graph; unfortunately, the size of this data structure can be quadratic in the size of the program, even for some structured programs. In this paper, we introduce a data structure called the augmented post-dominator tree (APT) which is constructed in space and time proportional to the size of the program, and which can answer control dependence queries in time proportional to the size of the output. Therefore, APT is an optimal representation of control dependence. We also show that using APT, we can compute SSA graphs, as well as sparse dataflow evaluator graphs, in time proportional to the size of the program. Finally, we put APT in perspective by showing that it can be viewed as a factored representation of control dependence graph in which filtered search is used to answer queries.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99a2bb7602d97022860a9

A linear-time algorithm is presented for finding dominators in control flow graphs.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99a3cb7602d970229b96b

Most parallel databases exploit two types of parallelism: intra-query parallelism and inter-transaction concurrency. Between these two cases lies another type of parallelism: inter-query parallelism within a transaction or application. Exploiting inter-query parallelism requires either compiler support to automatically parallelize the existing embedded query programs, or programming support to write explicitly parallel query programs. In this paper, we present compiler analysis to automatically detect parallelism in the embedded query programs. We present compiler algorithms for detecting dependences in such programs. We show that the properties of some aggregate functions such as MIN and MAX can help reduce statically computed dependences.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99a4eb7602d97022b2f27

On the one hand, compilers for secure computation protocols, such as FairPlay or FairPlayMP, have significantly simplified the development of such protocols. On the other hand, optimized protocols with high performance for special problems demand manual development and security verification. The question considered in this paper is: Can we construct a compiler that produces optimized protocols? We present an optimization technique based on logic inference about what is known from input and output. Using the example of median computation we can show that our program analysis and rewriting technique translates a FairPlay program into an equivalent -- in functionality and security -- program that corresponds to the protocol by Aggarwal et al. Nevertheless our technique is general and can be applied to optimize a wide variety of secure computation protocols.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99a5cb7602d97022c6d68

To achieve its objectives, the MicroUnity mediaprocessor requires a software development environment which allows development at high levels of abstraction, yet provides access to the high-performance processor resources where necessary. The components of this environment include a symbolic development front-end for mathematical algorithms, an optimizing C compiler, a fast, cycle-accurate simulator, source debugger, performance analysis tools, operating systems, and libraries. In this paper, we describe these components, as well as some of our future plans for them.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99a67b7602d97022d6402

While the popularity of using high-level programming languages such as MATLAB for scientific and engineering applications continues to grow, its poor performance compared to traditional languages such as Fortran or C continues to impede its deployment in full-scale simulations and data analysis. Additionally, its poor memory performance limits its performance. To ameliorate performance, we have been developing a MATLAB and Octave compiler that improves performance of MATLAB code by performing type inference and using the resulting type information to remove common bottlenecks. We observe that unlike past results, scalarizing array statements, instead of vectorizing scalar statements, is more fruitful when compiling MATLAB to C or C++. Two important situations where such scalarization helps is in expressions containing array subscripts and sequences of related array statements. In both cases, it is possible to generate fused loops and replace array temporaries by scalars, thus reducing the memory bandwidth pressure. Additional array temporaries are obviated in the case of array subscripts. Further, starting with vectorized statements guarantees that the resulting loops can be parallelized, creating opportunities for a mix of thread-level and instruction-level parallelism as well as GPU execution. We have implemented this strategy in a MATLAB compiler that compiles portions of MATLAB to C++ or CUDA C. Evaluation results on a set of benchmarks selected from diverse domains shows speed improvements ranging from 1.5x to almost 17x on an eight-core Intel Core 2 Duo machine.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99a74b7602d97022e4106

In order to meet the high throughput requirements of applications exhibiting high ILP, VLIW ASIPs may increasingly include large numbers of functional units(FUs). Unfortunately, ”switching“ data through register files shared by large numbers of FUs quickly becomes a dominant cost/ performance factor suggesting that clustering smaller number of FUs around local register files may be beneficial even if data transfers are required among clusters. With such machines in mind, we propose a compiler transformation, predicated switching, which enables aggressive speculation while leveraging the penalties associated with inter-cluster communication to achieve gains in performance. Based on representative benchmarks, we demonstrate that this novel technique is particularly suitable for application specific clustered machines aimed at supporting high ILP as compared to state-of-the-art approaches.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99a7fb7602d97022f4130

As a type-safe program language, Java requires bounds checks of array accesses. Whenever an array element is accessed, a cmp (compare) instruction is executed to check whether the index value is within the valid bounds. Array bounds checks may prevent many useful optimizations because of precise exception. We present a new ABCE (array bounds check elimination) algorithm to eliminate redundant checks based on sparse representation for a Java static compiler. In contrast to other approaches performing in JVMs, we adhere to the design principle of the static compiler to optimize scientific Java applications. The algorithm is a light-weight algorithm working on an intermediate representation in static single assignment form. It fully removes bounds checks if it can be proven that they never fail. Whenever possible, it moves bounds checks out of loops to reduce the total number of executed checks. If such a check fails, the executing program branches into the unmodified loop to preserve the exception semantics of Java. For the scientific SciMark 2.0 benchmark suite, this algorithm removes on average 76% of bounds check instructions. The evaluation shows a speedup near to the theoretical maximum for LU test case.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99a8cb7602d9702301dd0

Loop leaping is the colloquial name given to a form of program analysis in which summaries are derived for nested loops starting from the innermost loop and proceeding in a bottom-up fashion considering one more loop at a time. Loop leaping contrasts with classical approaches to finding loop invariants that are iterative; loop leaping is compositional requiring each stratum in the nest of loops to be considered exactly once. The approach is attractive in predicate abstraction where disjunctive domains are increasingly used that present long ascending chains. This paper proposes a simple and an efficient approach for loop leaping for these domains based on viewing loops as closure operators.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99a92b7602d9702309854

We present practical approximation methods for computing and representing interprocedural aliases for a program written in a language that includes pointers, reference parameters, and recursion. We present the following contributions: (1) a framework for interprocedural pointer alias analysis that handles function pointers by constructing the program call graph while alias analysis is being performed; (2) a flow-sensitive interprocedural pointer alias analysis algorithm; (3) a flow-insensitive interprocedural pointer alias analysis algorithm; (4) a flow-insensitive interprocedural pointer alias analysis algorithm that incorporates kill information to improve precision; (5) empirical measurements of the efficiency and precision of the three interprocedural alias analysis algorithms.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99a92b7602d970230b185

Existing Java verifiers perform an iterative data-flow analysis to discover the unambiguous type of values stored on the stack or in registers. Our novel verification algorithm uses abstract interpretation to obtain definition/use information for each register and stack location in the program, which in turn is used to transform the program into Static Single Assignment form. In SSA, verification is reduced to simple type compatibility checking between the definition type of each SSA variable and the type of each of its uses. Inter-adjacent transitions of a value through stack and registers are no longer verified explicitly. This integrated approach is more efficient than traditional bytecode verification but still as safe as strict verification, as overall program correctness can be induced once the data flow from each definition to all associated uses is known to be type-safe.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99a97b7602d970230c526

Induction variable analysis is an important part of the symbolic analysis in parallelizing compilers. Induction variables can be formed by FOR or DO loops within procedures or loops of recursive procedure calls. This paper presents an algorithm to find induction variables in formal parameters of procedures caused by recursive procedure calls. The compile-time knowledge of induction variables in formal parameters is essential to summarize array sections to be used for data dependency testing and parallelization

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99a9eb7602d9702313926

Research related to alias protection and related concepts, such as, confined types and ownership types has a long tradition and is a promising concept for the design and implementation of more reliable and secure software. Unfortunately, the use of these concepts is not widespread as most implementations are proofs of concept and fall short with respect to the integration with standard software development tools and processes.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99a9fb7602d970231723b

The synchronous language Esterel provides deterministic concurrency by adopting a semantics in which threads march in step with a global clock and communicate in a very disciplined way. Its expressive power comes at a cost, however: it is a difficult language to compile into machine code for standard von Neumann processors. The open-source Columbia Esterel Compiler is a research vehicle for experimenting with new code generation techniques for the language. Providing a front-end and a fairly generic concurrent intermediate representation, a variety of back-ends have been developed. We present three of the most mature ones, which are based on program dependence graphs, dynamic lists, and a virtual machine. After describing the very different algorithms used in each of these techniques, we present experimental results that compares twenty-four benchmarks generated by eight different compilation techniques running on seven different processors.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99ad1b7602d97023552de

We present techniques that allow strength reduction to be performed concurrently with partial redundancy elimination in the SSAPRE framework. By sharing the characteristics inherent to SSAPRE, the resulting strength reduction algorithm exhibits many interesting attributes. We compare various aspects of the new strength reduction algorithm with previous strength reduction algorithms. We also outline and discuss our implementation of the closely related linear function test replacement optimization under the same framework.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99adcb7602d970235ff64

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99ae2b7602d970236b2e9

Jalapeño is a virtual machine for JavaTM servers written in the Java language. To be able to address the requirements of servers (performance and scalability in particular), Jalapeño was designed "from scratch" to be as self-sufficient as possible. Jalapeño's unique object model and memory layout allows a hardware null-pointer check as well as fast access to array elements, fields, and methods. Run-time services conventionally provided in native code are implemented primarily in Java. Java threads are multiplexed by virtual processors (implemented as operating system threads). A family of concurrent object allocators and parallel type-accurate garbage collectors is supported. Jalapeño's interoperable compilers enable quasi-preemptive thread switching and precise location of object references. Jalapeño's dynamic optimizing compiler is designed to obtain high quality code for methods that are observed to be frequently executed or computationally intensive.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99af7b7602d97023886f9

Many software compilers for embedded processors produce machine code of insufficient quality. Since for most applications software must meet tight code speed and size constraints, embedded software is still largely developed in assembly language. In order to eliminate this bottleneck and to enable the use of high-level language compilers also for embedded software, new code generation and optimization techniques are required. This paper describes a novel code generation technique for embedded processors with irregular data path architectures, such as typically found in fixed-point DSPs. The proposed code generation technique maps data flow graph representation of a program into highly efficient machine code for a target processor modeled by instruction set behavior. High code quality is ensured by tight coupling of different code generation phases. In contrast to earlier works, mainly based on heuristics, our approach is constraint-based. An initial set of constraints on code generation are prescribed by the given processor model. Further constraints arise during code generation based on decisions concerning code selection, register allocation, and scheduling. Whenever possible, decisions are postponed until sufficient information about a good decision has been collected. The constraints are active in the \\"background\\" and guarantee local satisfiability at any point of time during code generation. This mechanism permits to simultaneously cope with special-purpose registers and instruction level parallelism. We describe the detailed integration of code generation phases. The implementation is based on the constraint logic programming (CLP) language ECLiPSe. For a standard DSP, we show that the quality of generated code comes close to hand-written assembly code. Since the input processor model can be edited by the user, also retargetability of the code generation technique is achieved within a certain processor class.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99b04b7602d97023946b8

We present Runtime Automatic Speculative Parallelization (RASP), a technique for the dynamic extraction of speculative threads from a running application in a user-transparent fashion. By leveraging the idle cores in a CMP to analyze, optimize, and participate in the execution of a running sequential program, RASP enables a collection of simpler cores to achieve sequential performance on par with a significantly more complex core. In contrast to other systems for automatic speculative parallelization, RASP uses dynamic binary translation to optimize applications on-the-fly, without any need for recompilation or source code. RASP achieves these speedups without relying on special-purpose hardware support; RASP's dynamic profiling uses a clever variation on conventional performance monitoring, while RASP's speculative execution relies on the same simple hardware support for speculation that has been proposed for simplifying parallel programming. On a simulated cluster of four in-order cores, RASP accelerates SPEC2006 integer benchmarks by an average of 49%, with promising results for scientific and multimedia workloads as well.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99b04b7602d9702395260

This short paper is a summary of my doctoral research, which was conducted under Oege de Moor's supervision at the University of Oxford, starting October 2001. The thesis was submitted on September 29, 2006, and defended on January 18, 2007. The final version was submitted, after minor corrections, on June 15, 2007.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99b16b7602d97023abbcf

An obfuscation aims to transform a program, without affecting the functionality, so that some secret information within the program can be hidden for as long as possible from an adversary. Proving that an obfuscating transform is correct (i.e. it preserves functionality) is considered to be a challenging task. In this paper we show how data refinement can be used to specify imperative data obfuscations. An advantage of this approach is that we can establish a framework in which we can prove the correctness of our obfuscations. We demonstrate our framework by considering some examples from obfuscation literature. We show how to specify these obfuscations, prove that they are correct and produce generalisations.

53e9a81fb7602d97031681bc

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

53e99b16b7602d97023abf6a

ing with credit is permitted. To copy otherwise, to republish, to post on servers, or to redistribute to lists, requires priorspecific permission and/or a fee. Request permissions from Publications Dept, ACM Inc., fax +1(212)869-0481, or [email protected] Programmingi 3 (i 2 )j 3 (j 2 )k 3 (k 2 )if j 3 < 203i 4 (i 2 )j 4 (j 2 )k 4 (k 2 )return j 44i 7 (i 5 , i 6 )j 7 (j 8 , j 9 )k 7 (k 8 ,k 9 )71i 1 1j 1 1k 1 02i 2 (i 7 , i 1...