Adaptive Configuration Selection for Power-Constrained Heterogeneous Systems
Adaptive Configuration Selection for Power-Constrained Heterogeneous Systems
复制标题
DOI：
10.1109/icpp.2014.46
复制
发表时间：
2014-10
期刊：
影响因子：
--
通讯作者：
Peter E. Bailey;D. Lowenthal;Vignesh T. Ravi;B. Rountree;M. Schulz;B. Supinski
中科院分区：
文献类型：
--
作者：
Peter E. Bailey;D. Lowenthal;Vignesh T. Ravi;B. Rountree;M. Schulz;B. Supinski
随着功率成为高端超级计算机中越来越重要的设计因素，未来的系统可能会在远低于其峰值功率规格的功率限制下运行。这些限制将通过软件和硬件电源策略的组合来实施，这些策略将从系统级别向下过滤到各个节点。通过向用户提供功率封顶接口，硬件已经朝着这个方向发展。节点级别的功率/性能权衡对于最大限度地提高功率受限集群系统的性能至关重要，但由于构成节点硬件配置的许多交互架构功能和加速器，因此也很复杂。解决这一挑战的关键是准确的功率/性能模型，这将有助于从大量可用配置中选择正确的配置。在本文中，我们提出了一种新的方法来产生这样的模型离线使用核聚类和多元线性回归。我们的模型只需两次迭代即可选择配置，这比基于穷举搜索的策略具有显着优势。我们应用我们的模型来预测使用任意配置的不同应用程序的功率和性能，并表明我们的模型，当与硬件频率限制一起使用时，选择在给定功率限制下具有显着更高性能的配置，而不是单独使用频率限制。当应用于一系列应用中的36个计算内核时，我们的模型准确地预测了功率和性能，它保持了91%的最佳性能，同时满足88%的功率约束。当模型违反功率约束时，它在平均情况下仅超出约束6%，同时实现比oracle高54%的性能。
As power becomes an increasingly important design factor in high-end supercomputers, future systems will likely operate with power limitations significantly below their peak power specifications. These limitations will be enforced through a combination of software and hardware power policies, which will filter down from the system level to individual nodes. Hardware is already moving in this direction by providing power-capping interfaces to the user. The power/performance trade-off at the node level is critical in maximizing the performance of power-constrained cluster systems, but is also complex because of the many interacting architectural features and accelerators that comprise the hardware configuration of a node. The key to solving this challenge is an accurate power/performance model that will aid in selecting the right configuration from a large set of available configurations. In this paper, we present a novel approach to generate such a model offline using kernel clustering and multivariate linear regression. Our model requires only two iterations to select a configuration, which provides a significant advantage over exhaustive search-based strategies. We apply our model to predict power and performance for different applications using arbitrary configurations, and show that our model, when used with hardware frequency-limiting, selects configurations with significantly higher performance at a given power limit than those chosen by frequency-limiting alone. When applied to a set of 36 computational kernels from a range of applications, our model accurately predicts power and performance, it maintains 91% of optimal performance while meeting power constraints 88% of the time. When the model violates a power constraint, it exceeds the constraint by only 6% in the average case, while simultaneously achieving 54% more performance than an oracle.