Abstract
In multiprocessor systems, the gap between peak and effective performance has getting larger. To cope with this performance gap, it is important to use multigrain parallelism in addition to ordinary loop level parallelism. Also, effective use of memory hierarchy is important for the performance improvement of multiprocessor systems because the speed gap between processors and memories is getting larger.
This paper describes coarse grain task parallel processing that uses parallelism among macro-tasks like loops and subroutines considering cache optimization using data localization scheme. The proposed scheme is implemented on OSCAR automatic multigrain parallelizing compiler. OSCAR compiler generates OpenMP FORTRAN program realizing the proposed scheme from a sequential FORTRAN77 program. Its performance is evaluated on IBM RS6000 SP 604e High Node 8 processors SMP machine using SPEC95fp tomcatv, swim, mgrid. In the evaluation, the proposed coarse grain task parallel processing scheme with cache optimization gives us up to 1.3 times speedup on 1PE, 4.7 times speedup on 4PE and 8.8 times speedup on 8PE compared with a sequential processing time.
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Ishizaka, K., Obata, M., Kasahara, H. (2003). Coarse Grain Task Parallel Processing with Cache Optimization on Shared Memory Multiprocessor. In: Dietz, H.G. (eds) Languages and Compilers for Parallel Computing. LCPC 2001. Lecture Notes in Computer Science, vol 2624. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-35767-X_23
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DOI: https://doi.org/10.1007/3-540-35767-X_23
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