Applying Logistic Regression Model on HPX Parallel Loops
Applying Logistic Regression Model on HPX Parallel Loops
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发表时间：
2017
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通讯作者：
Zahra Khatami;Lukas Troska;Hartmut Kaiser;J. Ramanujam
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作者：
Zahra Khatami;Lukas Troska;Hartmut Kaiser;J. Ramanujam
许多并行应用程序的性能取决于循环级并行性。然而，手动并行化所有循环可能会降低并行化性能，因为有些循环无法在更多线程上进行理想的扩展。此外，手动设置块大小的开销可能会避免应用程序达到其最大并行性能。我们将说明如何应用机器学习技术来解决这些挑战。在本研究中，我们开发了一个能够自动捕获循环的静态和动态信息的框架。此外，我们提倡一种新的方法，通过考虑在我们的学习模型中实现的那些捕获的信息来确定应用程序中循环的执行策略和块大小。我们评估的执行结果表明，所提出的技术可以将执行过程加快45%。
The performance of many parallel applications depend on the loop-level parallelism. However, manually parallelizing all loops may result in degrading parallelization performance, as some of the loops cannot scale desirably on more number of threads. In addition, the overheads of manually setting chunk sizes might avoid an application to reach its maximum parallel performance. We illustrate how machine learning techniques can be applied to address these challenges. In this research, we develop a framework that is able to automatically capture the static and dynamic information of a loop. Moreover, we advocate a novel method for determining execution policy and chunk size of a loop within an application by considering those captured information implemented within our learning model. Our evaluated execution results show that the proposed technique can speed up the execution process up to 45%.