Challenges in the automatic parallelization of large-scale computational applications

Brian Armstrong; Rudolf Eigenmann

doi:10.1117/12.434876

27 July 2001 Challenges in the automatic parallelization of large-scale computational applications

Brian Armstrong, Rudolf Eigenmann

Proceedings Volume 4528, Commercial Applications for High-Performance Computing; (2001) https://doi.org/10.1117/12.434876
Event: ITCom 2001: International Symposium on the Convergence of IT and Communications, 2001, Denver, CO, United States

Abstract

Application test suites used in the development of parallelizing compilers typically include single-file programs and algorithm kernels. The challenges posed by full-scale commercial applications are rarely addressed. It is often assumed that automatic parallelization is not feasible in the presence of large, realistic programs. In this paper, we reveal some of the hurdles that must be crossed in order to enable these compilers to apply parallelization techniques to large-scale codes. We use a benchmark suite that has been specifically designed to exhibit the computing needs found in industry. The benchmarks are provided by the High Performance Group of the Standard Performance Evaluation Corporation (SPEC). They consist of a seismic processing application and a quantum level molecular simulation. Both applications exist in a serial and a parallel variant. In our studies we compare the parallel variants with the automatically parallelized, serial codes. We use the Polaris parallelizing compiler, which takes Fortran codes and inserts OpenMP directives around loops determined to be dependence-free. We have found five challenges faced by an automatic parallelizing compiler when dealing with full applications: modularity, legacy optimizations, symbolic analysis, array reshaping, and issues arising from input/output operations.

Citation Download Citation

Brian Armstrong and Rudolf Eigenmann "Challenges in the automatic parallelization of large-scale computational applications", Proc. SPIE 4528, Commercial Applications for High-Performance Computing, (27 July 2001); https://doi.org/10.1117/12.434876

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