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

Abstract

AbstractOpenMP 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. Copyright © 2002 John Wiley & Sons, Ltd.