Exploiting Data Locality on Scalable Shared Memory Machines with Data Parallel Programs

Abstract

The OpenMP Application Program Interface supports parallel programming on scalable symmetric multiprocessor machines (SMP) with a shared memory by providing the user with simple work-sharing directives for C/C++ and Fortran so that the compiler can 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. HPF, the de-facto standard for data parallel programming, offers a rich set of data distribution directives in order to exploit data locality, but has mainly been targeted towards distributed memory machines. In this paper we describe an optimized execution model for HPF programs on SMP machines that avails itself with the mechanisms provided by OpenMP for work sharing and thread parallelism while exploiting data locality based on user-specified distribution directives. This execution model has been implemented in the ADAPTOR HPF compilation system and experimental results verify the efficiency of the chosen approach.