Norbert Schmitt, James Bucek, John Beckett, Aaron Cragin, K. Lange, Samuel Kounev
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引用次数: 1
Abstract
The growth of cloud services leads to more and more data centers that are increasingly larger and consume considerable amounts of power. To increase energy efficiency, both the actual server equipment and the software must become more energy efficient. Software has a major impact on hardware utilization levels, and subsequently, the energy efficiency. While energy efficiency is often seen as identical to performance, we argue that this may not be necessarily the case. A sizable amount of energy could be saved, increasing energy efficiency by leveraging compiler optimizations but at the same time impacting performance and power consumption over time. We analyze the SPEC CPU 2017 benchmark suite with 43 benchmarks from different domains, including integer and floating-point heavy computations on a state-of-the-art server system for cloud applications. Our results show that power consumption displays more stable behavior if less compiler optimizations are used and also confirmed that performance and energy efficiency are different optimizations goals. Additionally, compiler optimizations possibly could be used to enable power capping on a software level and care must be taken when selecting such optimizations.
云服务的增长导致越来越多的数据中心变得越来越大,并消耗大量的电力。为了提高能源效率,实际的服务器设备和软件都必须变得更加节能。软件对硬件的利用水平以及随后的能源效率有很大的影响。虽然能效通常被视为等同于性能,但我们认为情况未必如此。可以节省大量的能源,通过利用编译器优化来提高能源效率,但同时随着时间的推移会影响性能和功耗。我们分析了SPEC CPU 2017基准测试套件,其中包括来自不同领域的43个基准测试,包括在最先进的云应用服务器系统上的整数和浮点繁重计算。我们的结果表明,如果使用较少的编译器优化,则功耗显示出更稳定的行为,并且还证实了性能和能源效率是不同的优化目标。此外,编译器优化可能用于在软件级别启用功率上限,在选择此类优化时必须小心。