HPC AI500：具有代表性、可重复且简单的HPC AI基准测试
HPC AI500: Representative, Repeatable and Simple HPC AI Benchmarking
摘要：
近年来，在商业和科学计算领域出现了大规模分布式深度学习算法（HPC AI）的趋势，其目标是加快训练时间，以达到最先进的质量。HPC AI基准测试加速了这一过程。不幸的是，大规模的HPC AI系统基准测试带来了严峻的挑战。本文提出了一种具有代表性、可重复且简单的HPC AI基准测试方法。在AIBench Training的17个AI工作负载中-迄今为止最全面的AI培训基准套件-我们选择了两个代表性和可重复的AI工作负载。选定的HPC AI基准测试包括商业和科学计算：图像分类和极端天气分析。为了对HPC AI系统进行排名，我们提出了一个名为Valid FLOPS的新指标，强调吞吐量性能和目标质量。规范、源代码、数据集和HPC AI 500排名数字可从\url{https://www.benchcouncil.org/HPCAI500/}公开获得。
英文摘要：
Recent years witness a trend of applying large-scale distributed deep learning algorithms (HPC AI) in both business and scientific computing areas, whose goal is to speed up the training time to achieve a state-of-the-art quality. The HPC AI benchmarks accelerate the process. Unfortunately, benchmarking HPC AI systems at scale raises serious challenges. This paper presents a representative, repeatable and simple HPC AI benchmarking methodology. Among the seventeen AI workloads of AIBench Training -- by far the most comprehensive AI Training benchmarks suite -- we choose two representative and repeatable AI workloads. The selected HPC AI benchmarks include both business and scientific computing: Image Classification and Extreme Weather Analytics. To rank HPC AI systems, we present a new metric named Valid FLOPS, emphasizing both throughput performance and a target quality. The specification, source code, datasets, and HPC AI500 ranking numbers are publicly available from \url{https://www.benchcouncil.org/HPCAI500/}.
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。
