Tal Ben-Nun, Maciej Besta, S. Huber, Alexandros Nikolaos Ziogas, D. Peter, Torsten Hoefler:
A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning
(IEEE, May 2019, Accepted at the 33rd IEEE International Parallel & Distributed Processing Symposium (IPDPS'19) )
Abstract
We introduce Deep500: the first customizable benchmarking infrastructure that
enables fair comparison of the plethora of deep learning frameworks,
algorithms, libraries, and techniques. The key idea behind Deep500 is its
modular design, where deep learning is factorized into four distinct
levels: operators, network processing, training, and distributed
training. Our evaluation illustrates that Deep500 is customizable
(enables combining and benchmarking different deep learning codes) and
fair (uses carefully selected metrics). Moreover, Deep500 is
fast (incurs negligible overheads), verifiable (offers
infrastructure to analyze correctness), and reproducible. Finally, as
the first distributed and reproducible benchmarking system for deep learning,
Deep500 provides software infrastructure to utilize the most powerful supercomputers for extreme-scale workloads.
Documents
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BibTeX
@inproceedings{deep500, author={Tal Ben-Nun and Maciej Besta and S. Huber and Alexandros Nikolaos Ziogas and D. Peter and Torsten Hoefler}, title={{A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning}}, year={2019}, month={May}, publisher={IEEE}, note={Accepted at the 33rd IEEE International Parallel \& Distributed Processing Symposium (IPDPS'19) }, source={http://www.unixer.de/~htor/publications/}, }
