基于并行区域重构的改进环级OpenMP程序实现

Shi'an Hu, Aixian Dong, Hongtu Ma
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引用次数: 0

摘要

在三种OpenMP程序模型的基础上,重点讨论了并行区域重构技术,以实现改进的环级OpenMP程序。平行区域重构是对平行区域进行扩展和合并。在重构并行区域时,需要注意两点,即保持优化前后的数据属性和数据依赖性。PPOPP的实验结果表明,经过平行区域重建后,lu1k的改进率最高可达28.1%,erle64的改进率最低,约为1.87%。lu1k改进最大的原因是在1024次迭代的循环之外扩展了一个并行区域,这减少了1023倍的并行区域创建。实验结果表明,并行区域重建技术减少了并行区域的产生,提高了OpenMP程序的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Implement Improved Loop Level OpenMP Program Based on Parallel Region Reconstruction
Based on three OpenMP program models, the technology of parallel region reconstruction is mainly discussed to implement the improved loop level OpenMP program. Parallel region reconstruction is to expand and merge parallel regions. When reconstructing parallel regions, there are two things should be noted, that is to keep data attribute and data dependence before and after optimization. Experimental results of PPOPP show that after parallel region reconstruction, the improvement of lu1k is maximally up to 28.1%, and the improvement of erle64 is the lowest about 1.87%. The reason of lu1k's highest improvement is that a parallel region is expanded outside a loop of 1024 iterations, which reduce 1023 times of the parallel region creation. The experimental results indicate the technology of parallel region reconstruction reduces the creation of parallel region, and improves the performance of the OpenMP program.
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