SAIH：一种了解HPC系统AI性能趋势的可扩展评估方法
SAIH: A Scalable Evaluation Methodology for Understanding AI Performance Trend on HPC Systems
摘要：
新的人工智能（AI）技术加速了各种科学研究，例如，宇宙学、物理学和生物信息学，不可避免地成为高性能计算（HPC）系统上的重要工作负载类别。现有的人工智能基准测试倾向于定制公认的人工智能应用程序，以便根据数据集和人工智能模型来评估HPC系统在预定义问题大小下的人工智能性能。由于问题规模缺乏可扩展性，静态AI基准可能无法帮助理解HPC系统上不断发展的AI应用程序的性能趋势，特别是大规模系统上的科学AI应用程序。在本文中，我们提出了一种可扩展的评估方法（SAIH），用于分析HPC系统的AI性能趋势，并扩展定制AI应用程序的问题大小。为了实现可扩展性，SAIH建立了一套新的机制来增加问题的大小。随着数据和模型的不断扩展，我们可以研究HPC系统上AI性能的趋势和范围，并进一步诊断系统瓶颈。为了验证我们的方法，我们增加了一个宇宙AI应用程序，以评估一个配备GPU的真正HPC系统作为SAIH的案例研究。
英文摘要：
Novel artificial intelligence (AI) technology has expedited various scientific research, e.g., cosmology, physics and bioinformatics, inevitably becoming a significant category of workload on high performance computing (HPC) systems. Existing AI benchmarks tend to customize well-recognized AI applications, so as to evaluate the AI performance of HPC systems under predefined problem size, in terms of datasets and AI models. Due to lack of scalability on the problem size, static AI benchmarks might be under competent to help understand the performance trend of evolving AI applications on HPC systems, in particular, the scientific AI applications on large-scale systems. In this paper, we propose a scalable evaluation methodology (SAIH) for analyzing the AI performance trend of HPC systems with scaling the problem sizes of customized AI applications. To enable scalability, SAIH builds a set of novel mechanisms for augmenting problem sizes. As the data and model constantly scale, we can investigate the trend and range of AI performance on HPC systems, and further diagnose system bottlenecks. To verify our methodology, we augment a cosmological AI application to evaluate a real HPC system equipped with GPUs as a case study of SAIH.
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。
