@article {mattson2019mlperf,
	title = {Mlperf training benchmark},
	journal = {arXiv preprint arXiv:1910.01500},
	year = {2019},
	abstract = {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{\textquoteright}s efficacy at driving performance and scalability improvements across two rounds of results from multiple vendors.},
	url = {https://doi.org/10.48550/arXiv.1910.01500},
	author = {Mattson, Peter and Cheng, Christine and Coleman, Cody and Diamos, Greg and Micikevicius, Paulius and Patterson, David and Tang, Hanlin and Gu  Wei and Peter Bailis and Bittorf, Victor and David Brooks and Dehao Chen and Debo Dutta and Udit Gupta and Kim Hazelwood and Andy Hock and Xinyuan Huang and Daniel Kang and Kanter, David and Naveen Kumar and Jeffery Liao and Deepak Narayanan and Tayo Oguntebi and Gennady Pekhimenko and Lillian Pentecost and Vijay Janapa Reddi and Taylor Robie and Tom St John and Carole-Jean Wu and Lingjie Xu and Cliff Young and Matei Zaharia}
}
