Optimal loop parallelization for maximizing iteration-level parallelism

Duo Liu, Z. Shao, M. Wang, M. Guo, Jingling Xue
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引用次数: 18

Abstract

This paper solves the open problem of extracting the maximal number of iterations from a loop that can be executed in parallel on chip multiprocessors. Our algorithm solves it optimally by migrating the weights of parallelism-inhibiting dependences on dependence cycles in two phases. First, we model dependence migration with retiming and formulate this classic loop parallelization into a graph optimization problem, i.e., one of finding retiming values for its nodes so that the minimum non-zero edge weight in the graph is maximized. We present our algorithm in three stages with each being built incrementally on the preceding one. Second, the optimal code for a loop is generated from the retimed graph of the loop found in the first phase. We demonstrate the effectiveness of our optimal algorithm by comparing with a number of representative non-optimal algorithms using a set of benchmarks frequently used in prior work.
优化循环并行化最大化迭代级并行
本文解决了在芯片多处理器上可并行执行的循环中提取最大迭代次数的开放性问题。我们的算法通过在两个阶段的依赖循环中迁移并行抑制依赖的权重来最优地解决该问题。首先,我们用重定时对依赖迁移进行建模,并将这种经典的循环并行化形成一个图优化问题,即为其节点寻找重定时值,使图中最小的非零边权最大化。我们将算法分为三个阶段,每个阶段都是在前一个阶段的基础上逐步构建的。其次,循环的最优代码是从第一阶段找到的循环的重新定时图中生成的。我们通过使用一组在先前工作中经常使用的基准,将我们的最优算法与许多具有代表性的非最优算法进行比较,从而证明了我们的最优算法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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