ARCS: Adaptive Runtime Configuration Selection for Power-Constrained OpenMP Applications
ARCS: Adaptive Runtime Configuration Selection for Power-Constrained OpenMP Applications
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ARCS：针对功耗受限的 OpenMP 应用程序的自适应运行时配置选择
DOI：
10.1109/cluster.2016.39
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
O. Sarood
中科院分区：
文献类型：
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作者：
Md Abdullah Shahneous Bari;Nicholas Chaimov;A. Malik;K. Huck;B. Chapman;A. Malony;O. Sarood
功率是Exascale高性能计算的最关键资源。将来，系统管理员可能必须注意不同工作负载下机器的功耗。因此，每个应用程序可能必须使用分配的功率预算运行。因此，在未来机器上实现最佳性能需要受功率限制的最佳性能。此额外的性能要求不应是HPC〜（高性能计算）应用程序开发人员的责任。优化给定功率预算的性能应该是高性能系统软件堆栈的责任。现代机器允许CPU和内存的电源封盖来实施功率预算策略。在给定功率水平上找到节点的最佳运行时环境对于获得最佳性能很重要。本文介绍了ARCS（自适应运行时配置选择）Frameworkworkthat自动在给定功率级别上为每个OpenMpparallel区域选择最佳的运行时配置。该框架使用OMPT（OpenMP工具）API，APE（EXASCALE的自主性能环境）和Active Harmony Frameworksto探索配置搜索空间，并选择最佳的线程数量，调度策略和块大小，用于运行时的给定功率水平。我们使用NAS并行基准测试弧，以及与Intel Sandybridge和IBM Power多核架构的代理应用Lulesh。我们表明，对于给定的功率水平，有效的OpenMP运行时参数选择可以分别提高应用程序的执行时间和能源消耗，分别高达40％和42％。
Power is the most critical resource for the exascale high performance computing. In the future, system administrators might have to pay attention to the power consumption of the machine under different work loads. Hence, each application may have to run with an allocated power budget. Thus, achieving the best performance on future machines requires optimal performance subject to a power constraint. This additional performance requirement should not be the responsibility of HPC~(High Performance Computing) application developers. Optimizing the performance for a given power budget should be the responsibility of high-performance system software stack. Modern machines allow power capping of CPU and memory to implement power budgeting strategy. Finding the best runtime environment for a node at a given power level is important to get the best performance. This paper presents ARCS (Adaptive Runtime Configuration Selection) frameworkthat automatically selects the best runtime configuration for each OpenMPparallel region at a given power level. The framework uses OMPT (OpenMP Tools) API, APEX(Autonomic Performance Environment for eXascale), and Active Harmony frameworksto explore configuration search space and selects the best number of threads, scheduling policy, and chunk size for a given power level at run-time. We test ARCS using the NAS Parallel Benchmark, and proxy application LULESH with Intel Sandybridge, and IBM Power multi-core architectures. We show that for a given power level, efficient OpenMP runtime parameter selection can improve the execution time and energy consumption of an application up to 40% and 42% respectively.