Languages and Compilers for Parallel Computing - 27th International Workshop, LCPC 2014, Hillsboro, OR, USA, September 15-17, 2014, Revised Selected Papers
Languages and Compilers for Parallel Computing - 27th International Workshop, LCPC 2014, Hillsboro, OR, USA, September 15-17, 2014, Revised Selected Papers
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并行计算的语言和编译器 - 第 27 届国际研讨会，LCPC 2014，美国俄勒冈州希尔斯伯勒，2014 年 9 月 15-17 日，修订后的精选论文
DOI：
10.1007/978-3-319-17473-0_14
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Emani M
中科院分区：
文献类型：
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作者：
Emani M
并行程序越来越多地在高度动态的环境中执行，其中将程序并行性映射到动态变化的系统资源是具有挑战性的。传统的离线编译器方法利用程序知识，但忽略了运行时环境。在线运行时方法动态地适应资源，但忽略程序结构。此外，在程序执行期间，没有机制来检测和提高这些方法的效率。提出了一种基于在线变化检测的运行时映射方法。它将线程的运行时调度建模为马尔可夫决策过程，并利用离线训练模型来预测基于代码和环境特征的最佳线程映射。然后，它开发了一种新的方法，其中环境预测器的准确性被用作模型质量的度量，随着时间的推移调整线程映射。在评估我们的方案与不同的外部工作负载和硬件可用性，我们实现了2.14倍的平均加速比提高了默认的OpenMP策略，1.58倍的在线方法和1.32倍的最先进的离线训练模型。
Parallel programs increasingly execute in highly dynamic environments where mapping program parallelism to dynamically varying system resources is challenging. Traditional offline compiler approaches exploit program knowledge but ignore the runtime environment. Online runtime approaches dynamically adapt to resources but ignore program structure. Furthermore, there is no mechanism to detect and improve the efficiency of these approaches during program execution. This paper develops a new runtime mapping approach based on online change detection. It models runtime scheduling of threads as a Markov Decision Process and exploits an offline trained model to predict the best thread mapping based on both code and environment features. It then develops a novel approach where the accuracy of an environment predictor is used as a measure of the model quality, adjusting thread mapping over time. On evaluating our scheme with varying external workloads and hardware availability, we achieve an average speedup improvement of 2.14x over the default OpenMP policy, 1.58x over an online approach and 1.32x over a state-of-the-art offline trained model.