document: High-performance numerical pricing methods The pricing of financial derivatives is an important field in finance and constitutes a major component of financial management applications . The uncertainty of future events often makes analytic approaches infeasible and , hence , time-consuming numerical simulations are required . In the Aurora Financial Management System , pricing is performed on the basis of lattice representations of stochastic multidimensional scenario processes using the Monte Carlo simulation and Backward Induction methods , the latter allowing for the exploitation of shared-memory parallelism . We present the parallelization of a Backward Induction numerical pricing kernel on a cluster of SMPs using HPF + , an extended version of High-Performance Fortran . Based on language extensions for specifying a hierarchical mapping of data onto an SMP cluster , the compiler generates a hybrid-parallel program combining distributed-memory and shared-memory parallelism . We outline the parallelization strategy adopted by the VFC compiler and present an experimental evaluation of the pricing kernel on an NEC SX-5 vector supercomputer and a Linux SMP cluster , comparing a pure MPI version to a hybrid-parallel MPI/OpenMP version
doc_bio_tags: O O B O O B O O O O O O O O B O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B O O O O O O O O O O O O O O O B I I O B I I O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O
extractive_keyphrases: finance financial management pricing monte carlo simulation backward induction methods
abstractive_keyphrases: aurora financial management system stochastic processes numerical pricing kernel derivative pricing investment strategies