13 citations · 24 across the 5 of their papers we have counts for
5 papers
HPC AI500: Representative, Repeatable and Simple HPC AI Benchmarking
Zihan Jiang, Wanling Gao, Fei Tang +6
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 tr…
HPC AI500: The Methodology, Tools, Roofline Performance Models, and Metrics for Benchmarking HPC AI Systems
Zihan Jiang, Lei Wang, Xingwang Xiong +6
The recent years witness a trend of applying large-scale distributed deep learning in both business and scientific computing areas, whose goal is to speed up the training time to a…
AIBench Scenario: Scenario-distilling AI Benchmarking
Wanling Gao, Fei Tang, Jianfeng Zhan +7
Modern real-world application scenarios like Internet services consist of a diversity of AI and non-AI modules with huge code sizes and long and complicated execution paths, which…
AIBench Training: Balanced Industry-Standard AI Training Benchmarking
Fei Tang, Wanling Gao, Jianfeng Zhan +30
Earlier-stage evaluations of a new AI architecture/system need affordable benchmarks. Only using a few AI component benchmarks like MLPerfalone in the other stages may lead to misl…
AIBench: An Agile Domain-specific Benchmarking Methodology and an AI Benchmark Suite
Wanling Gao, Fei Tang, Jianfeng Zhan +31
Domain-specific software and hardware co-design is encouraging as it is much easier to achieve efficiency for fewer tasks. Agile domain-specific benchmarking speeds up the process…