Deep500: ETH Researchers Introduce New Deep Learning Benchmark for HPC

ETH researchers have developed a new deep learning benchmarking environment – Deep500 – they say is "the first distributed and reproducible benchmarking system for deep learning, [and] provides software infrastructure to utilize the most powerful supercomputers for extreme-scale workloads." The researchers used CSCS Piz Daint supercomputer in developing the benchmark, have made the code freely available on GitHub, and last week published a detailed analysis of their approach (A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning)[i]. "Deep500 [is] the first customizable bench- marking infrastructure that enables fair comparison of the plethora of deep learning frameworks, algorithms, libraries, and techniques," write the researchers. "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."