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Oral
MLPerf Training Benchmark
Peter Mattson · Christine Cheng · Gregory Diamos · Cody Coleman · Paulius Micikevicius · David Patterson · Hanlin Tang · Gu-Yeon Wei · Peter Bailis · Victor Bittorf · David Brooks · Dehao Chen · Debo Dutta · Udit Gupta · Kim Hazelwood · Andy Hock · Xinyuan Huang · Daniel Kang · David Kanter · Naveen Kumar · Jeffery Liao · Deepak Narayanan · Tayo Oguntebi · Gennady Pekhimenko · Lillian Pentecost · Vijay Janapa Reddi · Taylor Robie · Tom St John · Carole-Jean Wu · Lingjie Xu · Cliff Young · Matei Zaharia

Tue Mar 03 03:45 PM -- 04:10 PM (PST) @ Ballroom A
in Efficient Model Training 2 »

Machine learning is experiencing an explosion of software and hardware solutions, and needs industry-standard performance benchmarks to drive design and enable competitive evaluation. However, machine learning training presents a number of unique challenges to benchmarking that do not exist in other domains: (1) some optimizations that improve training throughput actually increase time to solution, (2) training is stochastic and time to solution has high variance, and (3) the software and hardware systems are so diverse that they cannot be fairly benchmarked with the same binary, code, or even hyperparameters. We present MLPerf, a machine learning benchmark that overcomes these challenges. We quantitatively evaluate the efficacy of MLPerf in driving community progress on performance and scalability across two rounds of results from multiple vendors.

Author Information

Peter Mattson (Google)
Christine Cheng (Intel)
Gregory Diamos (Baidu)
Cody Coleman (Stanford)
Paulius Micikevicius (NVIDIA)
David Patterson (Google)
Hanlin Tang (Intel Corporation)
Gu-Yeon Wei
Peter Bailis (Stanford University)
Victor Bittorf (Google)
David Brooks (Harvard University)
Dehao Chen (Google)
Debo Dutta (Cisco Systems, Inc.)
Udit Gupta (Harvard University)
Kim Hazelwood (Facebook AI)
Andy Hock (Cerebras Systems)
Xinyuan Huang (Cisco Systems, Inc.)
Daniel Kang (Stanford University)
David Kanter (RWI)
Naveen Kumar (Google)
Jeffery Liao (Synopsys)
Deepak Narayanan (Stanford)
Tayo Oguntebi (Google LLC)
Gennady Pekhimenko (University of Toronto)
Lillian Pentecost (Harvard University)
Vijay Janapa Reddi (Harvard University)
Taylor Robie (Google)
Tom St John (Tesla)
Carole-Jean Wu (Facebook AI)
Lingjie Xu (Alibaba)
Cliff Young (google.com)
Matei Zaharia (Stanford and Databricks)

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