AIPerf: Automated Machine Learning as an AI-HPC Benchmark

作     者：Zhixiang Ren Yongheng Liu Tianhui Shi Lei Xie Yue Zhou Jidong Zhai Youhui Zhang Yunquan Zhang Wenguang Chen 

作者机构：Peng Cheng National LaboratoryShenzhen 518000China Department of Computer Science and TechnologyTsinghua UniversityBeijing 100084China Institute of Computing TechnologyChinese Academy of SciencesBeijing 100086China 

出 版 物：《Big Data Mining and Analytics》 (大数据挖掘与分析（英文）)

年 卷 期：2021年第4卷第3期

页      面：208-220页

核心收录：

学科分类：0810[工学-信息与通信工程] 1205[管理学-图书情报与档案管理] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 0839[工学-网络空间安全] 081104[工学-模式识别与智能系统] 08[工学] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：High-Performance Computing(HPC) Artificial Intelligence(AI) automated machine learning 

摘      要：The plethora of complex Artificial Intelligence(AI)algorithms and available High-Performance Computing(HPC)power stimulates the expeditious development of AI components with heterogeneous ***,the need for cross-stack performance benchmarking of AI-HPC systems has rapidly *** particular,the de facto HPC benchmark,LINPACK,cannot reflect the AI computing power and input/output performance without a representative *** popular AI benchmarks,such as MLPerf,have a fixed problem size and therefore limited *** address these issues,we propose an end-to-end benchmark suite utilizing automated machine learning,which not only represents real AI scenarios,but also is auto-adaptively scalable to various scales of *** implement the algorithms in a highly parallel and flexible way to ensure the efficiency and optimization potential on diverse systems with customizable *** utilize Operations Per Second(OPS),which is measured in an analytical and systematic approach,as a major metric to quantify the AI *** perform evaluations on various systems to ensure the benchmark’s stability and scalability,from 4 nodes with 32 NVIDIA Tesla T4(56.1 Tera-OPS measured)up to 512 nodes with 4096 Huawei Ascend 910(194.53 Peta-OPS measured),and the results show near-linear weak *** a flexible workload and single metric,AIPerf can easily scale on and rank AI-HPC,providing a powerful benchmark suite for the coming supercomputing era.