自动软件优化中的挑战：能源效率案例
Challenges in Automatic Software Optimization: the Energy Efficiency Case
AI总结：
本文以能源效率为例，探讨自动软件优化面临的挑战，指出能源感知代码的必要性，并提出未来在两种粒度上进行能源优化的研究方向。
摘要：
随着百亿亿次（Exascale）计算能力的出现，使得超级计算机每秒能够执行至少$10^{18}$次IEEE 754双精度（64位）运算，人们对高性能计算代码的能源消耗提出了许多担忧。最近，由橡树岭国家实验室运营的Frontier成为第一台突破百亿亿次壁垒的超级计算机。它总共包含9,408个CPU、37,632个GPU和8,730,112个核心。这台世界领先的超级计算机消耗约21兆瓦电力，这确实非常了不起，因为它此前在Green500榜单上排名第一，直到最近被取代。之前的Green500榜首机器，日本的MN-3，每瓦提供39.38千兆次浮点运算，而Frontier每瓦提供62.68千兆次浮点运算。所有这些基础设施和硬件改进只是冰山一角。现在需要能源感知代码来最小化分布式和/或多线程软件的能源消耗。例如，在节点内通信期间，数据移动瓶颈占系统能源消耗的$35-60\%$。在HPC环境中，通过节点间通信还会消耗额外的能源。本立场文件旨在介绍未来的研究方向，以进入能源感知软件时代。本文组织如下。首先，我们介绍有关测量和能源优化的相关工作。然后，我们建议关注能源优化中两个不同粒度的层次。
英文摘要：
With the advent of the Exascale capability allowing supercomputers to perform at least $10^{18}$ IEEE 754 Double Precision (64 bits) operations per second, many concerns have been raised regarding the energy consumption of high-performance computing code. Recently, Frontier operated by the Oak Ridge National Laboratory, has become the first supercomputer to break the exascale barrier. In total, contains 9,408 CPUs, 37,632 GPUs, and 8,730,112 cores. This world-leading supercomputer consumes about 21 megawatts which is truly remarkable as was also ranked first on the Green500 list before being recently replaced. The previous top Green500 machine, MN-3 in Japan, provided 39.38 gigaflops per watt, while the delivered 62.68 gigaflops per watt. All these infrastructure and hardware improvements are just the tip of the Iceberg. Energy-aware code is now required to minimize the energy consumption of distributed and/or multi-threaded software. For example, the data movement bottleneck is responsible for $35-60\%$ of a system's energy consumption during intra-node communication. In an HPC environment, additional energy is consumed through inter-node communication. This position paper aims to introduce future research directions to enter now in the age of energy-aware software. The paper is organized as follows. First, we introduce related works regarding measurement and energy optimization. Then we propose to focus on the two different levels of granularity in energy optimization.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
