Application-level power and performance characterization and optimization on IBM Blue Gene/Q systems
Application-level power and performance characterization and optimization on IBM Blue Gene/Q systems
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IBM Blue Gene/Q 系统上的应用程序级功耗和性能表征及优化
DOI：
10.1147/jrd.2012.2227580
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发表时间：
2013
期刊：
影响因子：
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通讯作者：
P. Bose
中科院分区：
文献类型：
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作者：
Ramon Bertran Monfort;Yutaka Sugawara;H. Jacobson;A. Buyuktosunoglu;P. Bose
为了理解当前计算机系统上的应用级功率/性能权衡，需要运行时监视能力。具体而言，需要非常细粒度的监控功能来获得有关功耗和性能行为的详细见解。执行细粒度的应用程序级特征化不仅有助于对应用程序代码进行微调，而且还增加了检测优化机会以改进下一代系统的机会。在本文中，我们描述了一种新的实验技术，用于在IBM Blue Gene®/Q平台上执行应用程序的自动细粒度功耗和性能表征。我们使用它来执行高分辨率的测量和随之而来的高性能计算系统的关键基准的特征：第一层红杉套件和Linpack。表征表明，这些基准表现出大的时间段，其中的内存和网络资源利用不足。我们量化这些时间来预测从未充分利用的资源（网络和内存）转移到处理器的功率的性能增益。我们探讨潜在的改善能源效率，如果节能和转移机制，在未来的发电系统中实施。
In order to understand application-level power/performance tradeoffs on current computer systems, runtime monitoring capabilities are needed. Specifically, very fine-grained monitoring capabilities are needed to gain detailed insights on power and performance behavior. Performing fine-grained application-level characterizations not only helps fine-tune application code, but it also increases the chances to detect optimization opportunities for improving next-generation systems. In this paper, we describe a new experimental technique to perform automatic fine-grained power and performance characterization of applications on the IBM Blue Gene®/Q platform. We use it to perform high-resolution measurements and attendant characterizations of key benchmarks for high-performance computing systems: the Tier-1 Sequoia suite and Linpack. The characterization shows that these benchmarks exhibit large time periods in which the memory and network resources are underutilized. We quantify these periods to predict the performance gains of shifting power from the underutilized resources (the network and the memory) to the processor. We explore potential improvements in energy efficiency if power-saving and shifting mechanisms are implemented in future generation systems.