Power and Energy Footprint of OpenMP Programs Using OpenMP Runtime API
Power and Energy Footprint of OpenMP Programs Using OpenMP Runtime API
复制标题
使用 OpenMP 运行时 API 的 OpenMP 程序的功耗和能耗
DOI：
10.1109/e2sc.2014.11
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发表时间：
2014
期刊：
影响因子：
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通讯作者：
B. Chapman
中科院分区：
文献类型：
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作者：
Anilkumar Nandamuri;A. Malik;A. Qawasmeh;B. Chapman
功率和能源已成为高性能计算（HPC）中硬件和软件设计的主要方面。最近，国防部（DOD）提出了一个限制，即应用程序和架构需要达到75 GFLOPS/WATT，以支持未来的任务。这需要为电力和能量优化做出重大研究。 OpenMP编程模型是HPC不可或缺的一部分。对电力和执行绩效的OpenMP计划的全面分析是一个活跃的研究领域。已经完成了在内核级别的功率性能方面表征OpenMP程序的工作。但是，在OpenMP事件级别上没有完成任何工作。由OpenMP标准委员会提出的OpenMP运行时API（ORA）允许绩效工具在OpenMP事件级别收集信息。在本文中，我们使用ORA进行功率和执行性能，对OpenMP程序进行了全面分析。使用Intel Sandybridge X86-64中的硬件计数器和运行平均电源限制（RAPL）能量传感器，我们测量OpenMP基准的功率和能量特性。我们的结果表明，最佳执行性能并不总是能提供最佳的能量使用。我们还发现，在障碍和队列中的等待时间是给定OpenMP计划高功耗的主要因素。我们的结果还表明，动态功率管理系统可以将其用于增强功率性能的独特模式。我们的结果表明，根据运行时环境，能源使用情况的差异很大。
Power and energy have become dominant aspects of hardware and software design in the High Performance Computing (HPC). Recently, the Department of Defense (DOD) has put a constraint that applications and architectures need to attain 75 GFLOPS/Watt in order to support the future missions. This requires a significant research effort towards power and energy optimization. OpenMP programming model is an integral part of HPC. Comprehensive analysis of OpenMP programs for power and execution performance is an active research area. Work has been done to characterize OpenMP programs with respect to power performance at kernel level. However, no work has been done at the OpenMP event level. OpenMP Runtime API (ORA), proposed by the OpenMP standard committee, allow a performance tool to collect information at the OpenMP event level. In this paper, we present a comprehensive analysis of the OpenMP programs using ORA for power and execution performance. Using hardware counters in the Intel SandyBridge x86-64 and Running Average Power Limit (RAPL) energy sensors, we measure power and energy characteristics of OpenMP benchmarks. Our results show that the best execution performance does not always give the best energy usage. We also find out that the waiting time at the barriers and in queue are the main factors for high power consumption for a given OpenMP program. Our results also show that there are unique patterns at the fine level that can be used by the dynamic power management system to enhance the power performance. Our results show substantial variation in energy usage depending upon the runtime environment.