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arxiv: 1910.01500 · v3 · pith:NZJQARV2 · submitted 2019-10-02 · cs.LG · cs.PF· stat.ML

MLPerf Training Benchmark

Peter Mattson , Christine Cheng , Cody Coleman , Greg Diamos , Paulius Micikevicius , David Patterson , Hanlin Tang , Gu-Yeon Wei
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Peter Bailis Victor Bittorf David Brooks Dehao Chen Debojyoti Dutta Udit Gupta Kim Hazelwood Andrew Hock Xinyuan Huang Atsushi Ike Bill Jia Daniel Kang David Kanter Naveen Kumar Jeffery Liao Guokai Ma Deepak Narayanan Tayo Oguntebi Gennady Pekhimenko Lillian Pentecost Vijay Janapa Reddi Taylor Robie Tom St. John Tsuguchika Tabaru Carole-Jean Wu Lingjie Xu Masafumi Yamazaki Cliff Young Matei Zaharia
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classification cs.LG cs.PFstat.ML
keywords trainingmlperfbenchmarkbenchmarkingchallengeshardwareperformancesoftware
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Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML. But ML training presents three unique benchmarking challenges absent from other domains: optimizations that improve training throughput can increase the time to solution, training is stochastic and time to solution exhibits high variance, and software and hardware systems are so diverse that fair benchmarking with the same binary, code, and even hyperparameters is difficult. We therefore present MLPerf, an ML benchmark that overcomes these challenges. Our analysis quantitatively evaluates MLPerf's efficacy at driving performance and scalability improvements across two rounds of results from multiple vendors.

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