论文标题
了解输入熵对FPU，CPU和GPU功率的影响
Understanding the Impact of Input Entropy on FPU, CPU, and GPU Power
论文作者
论文摘要
功率越来越成为高性能，GPU加速计算系统中的限制资源。了解功率变化的范围和来源对于在机架和系统峰值功率上设置逼真的界限以及开发最小能量的技术至关重要。尽管以前已经研究了制造过程中产生的变化和其他因素（例如算法），但该工作表明，该程序输入也会严重影响GPU上消耗的功率，而且还会影响CPU。对于同一算法（DGEMM基准）和具有不同矩阵值的输入大小，在NVIDIA AMPERE A100 GPU上观察到了高达67％的功率变化。我们的调查表明，用作矩阵元素的值，它们的位置和它们的独特性会强烈影响功耗。进一步讨论了这种结果对超级计算机性能和能源效率的影响。
Power is increasingly becoming a limiting resource in high-performance, GPU-accelerated computing systems. Understanding the range and sources of power variation is essential in setting realistic bounds on rack and system peak power, and developing techniques that minimize energy. While variations arising during manufacturing and other factors like algorithm among others have been previously studied, this work shows that the program inputs can also severely impact the power consumed not only on the GPU but also CPUs. Power variations of up to 67% were observed on an NVIDIA Ampere A100 GPU for the same algorithm (DGEMM benchmark) and input size with different matrix values. Our investigation shows that the values used as matrix elements, their position, and their uniqueness strongly influence power consumption. The implications of this result on supercomputer performance and energy efficiency are further discussed.
