Automatic speculative DOALL for clusters
Automatic speculative DOALL for clusters
复制标题
集群的自动推测 DOALL
DOI：
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发表时间：
2012
期刊：
影响因子：
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通讯作者：
David I. August
中科院分区：
文献类型：
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作者：
Hanjun Kim;Nick P. Johnson;Jae W. Lee;S. Mahlke;David I. August
群集的自动平行化是时间耗尽，易于错误的手动并行化的替代品。可以通过猜测和分析替换静态分析，从而破坏性能，自动化的自动投机性并不适用。自动投机DOALL（SPEC-DOALL）群集并行化系统，我们已经实现了一个原型并行化系统，称为群集规格，该系统由Spec-Doall Parallelized Compilers组成。 ，并且针对投机成功的情况进行了优化的运行时，群集规格可最大程度地减少投机运行时的通信和验证开销。 ，而没有猜测的DoAll仅实现了4.5倍的速度。
Automatic parallelization for clusters is a promising alternative to time-consuming, error-prone manual parallelization. However, automatic parallelization is frequently limited by the imprecision of static analysis. Moreover, due to the inherent fragility of static analysis, small changes to the source code can significantly undermine performance. By replacing static analysis with speculation and profiling, automatic parallelization becomes more robust and applicable. A naïve automatic speculative parallelization does not scale for distributed memory clusters, due to the high bandwidth required to validate speculation. This work is the first automatic speculative DOALL (Spec-DOALL) parallelization system for clusters. We have implemented a prototype automatic parallelization system, called Cluster Spec-DOALL, which consists of a Spec-DOALL parallelizing compiler and a speculative runtime for clusters. Since the compiler optimizes communication patterns, and the runtime is optimized for the cases in which speculation succeeds, Cluster Spec-DOALL minimizes the communication and validation overheads of the speculative runtime. Across 8 benchmarks, Cluster Spec-DOALL achieves a geomean speedup of 43.8x on a 120-core cluster, whereas DOALL without speculation achieves only 4.5x speedup. This demonstrates that speculation makes scalable fully-automatic parallelization for clusters possible.