Performance-constrained Distributed DVS Scheduling for Scientific Applications on Power-aware Clusters
Performance-constrained Distributed DVS Scheduling for Scientific Applications on Power-aware Clusters
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DOI：
10.1109/sc.2005.57
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发表时间：
2005-11
期刊：
影响因子：
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通讯作者：
Rong Ge;Xizhou Feng;K. Cameron
中科院分区：
文献类型：
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作者：
Rong Ge;Xizhou Feng;K. Cameron
如果不加以控制，使用数以万计的耗电元件来提高峰值性能的根本驱动力将导致无法承受的运营成本和故障率。高性能、功耗感知的分布式计算在不牺牲性能的情况下降低了分布式应用程序和系统的功耗和能源消耗。一般来说，我们使用DVS（动态电压调节）技术，现在可在高性能微处理器，以降低功耗并行应用程序运行时，CPU的峰值性能是没有必要的，由于负载不平衡，通信延迟等，我们提出了分布式性能导向的DVS调度策略，用于可扩展的功耗感知HPC集群。通过改变调度粒度，我们可以在不增加执行时间的情况下获得显著的节能效果（对于来自NAS PB的FT为36%）。我们创建了一个软件框架来实现和评估我们的各种技术，并显示性能导向调度始终比类似的方法节省更多的能量（几个代码近25%），对执行时间的影响较小（< 5%）。此外，我们说明了使用能量延迟产品自动选择分布式DVS的时间表，满足用户的需求。
Left unchecked, the fundamental drive to increase peak performance using tens of thousands of power hungry components will lead to intolerable operating costs and failure rates. High-performance, power-aware distributed computing reduces power and energy consumption of distributed applications and systems without sacrificing performance. Generally, we use DVS (Dynamic Voltage Scaling) technology now available in high-performance microprocessors to reduce power consumption during parallel application runs when peak CPU performance is not necessary due to load imbalance, communication delays, etc. We propose distributed performance-directed DVS scheduling strategies for use in scalable power-aware HPC clusters. By varying scheduling granularity we can obtain significant energy savings without increasing execution time (36% for FT from NAS PB). We created a software framework to implement and evaluate our various techniques and show performance-directed scheduling consistently saves more energy (nearly 25% for several codes) than comparable approaches with less impact on execution time (< 5%). Additionally, we illustrate the use of energy-delay products to automatically select distributed DVS schedules that meet users’ needs.