Improving Provisioned Power Efficiency in HPC Systems with GPU-CAPP

K. Straube, Jason Lowe-Power, C. Nitta, M. Farrens, V. Akella
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引用次数: 6

Abstract

In this paper we propose a microarchitectural technique called GPU Constant Average Power Processing (GPU-CAPP) that improves the power utilization of power provisioning-limited systems by using provisioned power as much as possible to accelerate computation on parallel work-loads. GPU-CAPP uses a flexible, decentralized control to ensure fast response times and the scalability required for increasingly parallel GPU designs. We use GPGPU-Sim and GPUWattch to simulate GPU-CAPP and evaluate its capabilities on a subset of the Rodinia benchmark suite. Overall, GPU-CAPP enables speedup by an average of 26% and 12% over equivalent fixed frequency systems at two power targets.
利用GPU-CAPP提高高性能计算系统的配置功率效率
在本文中,我们提出了一种称为GPU恒定平均功率处理(GPU- capp)的微架构技术,该技术通过尽可能多地使用已配置的功率来加速并行工作负载上的计算,从而提高了功率供应有限系统的功率利用率。GPU- capp使用灵活的分散控制来确保快速响应时间和日益并行的GPU设计所需的可扩展性。我们使用GPGPU-Sim和gpuwatch来模拟GPU-CAPP,并在Rodinia基准套件的一个子集上评估其功能。总体而言,GPU-CAPP在两个功率目标下比等效固定频率系统平均加速26%和12%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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