Details

Autor(en) / Beteiligte

• Ben-Nun, Tal
• Besta, Maciej
• Huber, Simon
• Ziogas, Alexandros Nikolaos
• Peter, Daniel
• Hoefler, Torsten

Titel

A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning

Ist Teil von

• 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2019, p.66-77

Ort / Verlag

IEEE

Erscheinungsjahr

2019

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE Xplore Digital Library

Beschreibungen/Notizen

• 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.