Optimizing irregular HPF applications using halos

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SUMMARYThe 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.

High‐performance numerical pricing methods

Moritsch

Benkner

2002

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Efficient parallel programming on scalable shared memory systems with High Performance Fortran

Benkner

Brandes

2002

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SUMMARY OpenMP offers a high-level interface for parallel programming on scalable shared memory (SMP)architectures. It provides the user with simple work-sharing directives while it relies on the compiler to generate parallel programs based on thread parallelism. However, the lack of language features for exploiting data locality often results in poor performance since the non-uniform memory access times on scalable SMP machines cannot be neglected. High Performance Fortran (HPF), the de-facto standard for data parallel programming, offers a rich set of data distribution directives in order to exploit data locality, but it has been mainly targeted towards distributed memory machines.In this paper we describe an optimized execution model for HPF programs on SMP machines that avails itself with mechanisms provided by OpenMP for work sharing and thread parallelism, while exploiting data locality based on user-specified distribution directives. Data locality does not only ensure that most memory accesses are close to the executing threads and are therefore faster, but it also minimizes synchronization overheads, especially in the case of unstructured reductions. The proposed shared memory execution model for HPF relies on a small set of language extensions, which resemble the OpenMP work-sharing features. These extensions, together with an optimized shared memory parallelization and execution model, have been implemented in the ADAPTOR HPF compilation system and experimental results verify the efficiency of the chosen approach.

Compiling data‐parallel programs for clusters of SMPs

Benkner¹,

Brandes²

2004

Clusters of shared-memory multiprocessors (SMPs) have become the most promising parallel computing platforms for scientific computing. However, SMP clusters significantly increase the complexity of user application development when using the low-level application programming interfaces MPI and OpenMP, forcing users to deal with both distributed-memory and shared-memory parallelization details. In this paper we present extensions of High Performance Fortran (HPF) for SMP clusters which enable the compiler to adopt a hybrid parallelization strategy, efficiently combining distributed-memory with shared-memory parallelism. By means of a small set of new language features, the hierarchical structure of SMP clusters may be specified. This information is utilized by the compiler to derive inter-node data mappings for controlling distributed-memory parallelization across the nodes of a cluster and intra-node data mappings for extracting shared-memory parallelism within nodes. Additional mechanisms are proposed for specifying inter- and intra-node data mappings explicitly, for controlling specific shared-memory parallelization issues and for integrating OpenMP routines in HPF applications. The proposed features have been realized within the ADAPTOR and VFC compilers. The parallelization strategy for clusters of SMPs adopted by these compilers is discussed as well as a hybrid-parallel execution model based on a combination of MPI and OpenMP. Experimental results indicate the effectiveness of the proposed features