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The R-LRPD Test: Speculative Parallelization of Partially Parallel Loops

机译:R-LRPD测试:部分平行环的推测平行化

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Current parallelizing compilers cannot identify a significant fraction of parallelizable loops because they have complex or statically insufficiently defined access patterns. In our previously proposed framework we have speculatively executed a loop as a doall, and applied a fully parallel data dependence test to determine if it had any cross-processor dependences; If the test failed, then the loop was re-executed serially. While this method exploits doall parallelism well, it can cause slowdowns for loops with even one cross-processor flow dependence because we have to re-execute sequentially. Moreover, the existing, partial parallelism of loops is not exploited. We now propose a generalization of our speculative doall parallelization technique, called the Recursive LRPD test, that can extract and exploit the maximum available parallelism of any loop and that limits potential slowdowns to the overhead of the runtime dependence test itself. In this paper we present the base algorithm and an analysis of the different heuristics for its practical application and a few experimental results on loops from Track, Spice, and FMA3D.
机译:当前并行编译器不能识别有效的分数并行循环,因为它们具有复杂或静态不足地定义的访问模式。在我们先前提出的框架中,我们推测了作为Doall的循环,并应用了完全并行数据依赖性测试,以确定它是否具有跨处理器的依赖项;如果测试失败,则循环串行重新执行。虽然这种方法利用的doall并行好,它可能会导致甚至一个横处理器流相依循环速度变慢,因为我们必须顺序地重新执行。此外,循环的现有部分并行性不会被利用。我们现在提出了推测DOALL并行化技术的概括,称为递归LRPD测试,可以提取和利用任何循环的最大可用行性,并限制运行时依赖测试本身的开销的潜在放缓。本文介绍了基础算法及其实际应用中不同启发式的分析,以及轨道,香料和FMA3D的循环上的一些实验结果。

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