异构平台电源效率优化技术分析

Yash Ukidave, D. Kaeli
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引用次数: 6

摘要

图形处理单元(gpu)已被广泛接受为许多高性能计算领域的首选计算平台。诸如OpenCL之类的编程标准的可用性被用来利用gpu提供的固有并行性。针对异构应用程序的源代码优化(如循环展开和平铺)报告了性能方面的巨大提升。然而,考虑到gpu的功耗,平台可以很快耗尽其功率预算。需要更好的解决方案来有效地利用异构系统上可用的电源效率。在这项工作中,我们评估了在异构应用程序上使用的不同优化的功率/性能效率。我们通过评估优化的能耗来分析功率/性能权衡。我们比较了4种不同的快速傅里叶变换实现中不同优化技术的性能。我们的研究涵盖了分立GPU和共享内存GPU (apu),并包括来自AMD (Llano apu和Southern Islands GPU), Nvidia (Kepler)和Intel (Ivy Bridge)的硬件作为测试平台。该研究确定了对功耗影响最大的架构和算法因素。我们探索了一系列应用程序优化,这些优化显示功耗增加了27%,但性能却提高了1.8倍以上。我们观察到,在不同的优化中,能源消耗的变化幅度为11%。我们将重点介绍不同的优化如何提高异构应用程序的执行性能,但也会影响应用程序的电源效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analyzing Optimization Techniques for Power Efficiency on Heterogeneous Platforms
Graphics processing units (GPUs) have become widely accepted as the computing platform of choice in many high performance computing domains. The availability of programming standards such as OpenCL are used to leverage the inherent parallelism offered by GPUs. Source code optimizations such as loop unrolling and tiling when targeted to heterogeneous applications have reported large gains in performance. However, given the power consumption of GPUs, platforms can exhaust their power budgets quickly. Better solutions are needed to effectively exploit the power-efficiency available on heterogeneous systems. In this work, we evaluate the power/performance efficiency of different optimizations used on heterogeneous applications. We analyze the power/performance trade-off by evaluating energy consumption of the optimizations. We compare the performance of different optimization techniques on 4 different Fast Fourier Transform implementations. Our study covers discrete GPUs and shared memory GPUs (APUs), and includes hardware from AMD (Llano APUs and the Southern Islands GPU), Nvidia (Kepler) and Intel (Ivy Bridge) as test platforms. The study identifies the architectural and algorithmic factors which can most impact power consumption. We explore arange of application optimizations which show an increase in power consumption by 27%, but result in more than a 1.8Xspeedup in performance. We observe a 11% variation in energy consumption among different optimizations. We highlight how different optimizations can improve the execution performance of a heterogeneous application, but also impact power efficiency of the application.
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