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

arXiv: https://arxiv.org/abs/1901.10183 · HTML: https://mlsanity.com/paper/1901.10183/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2019.

## Built on by (most cited first, 9 of 9)

- [Scientific Machine Learning Benchmarks](https://mlsanity.com/paper/2110.12773/) (2021)
- [Parallel and Distributed Graph Neural Networks: An In-Depth Concurrency Analysis](https://mlsanity.com/paper/2205.09702/) (2022)
- [MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems](https://mlsanity.com/paper/2110.11466/) (2021)
- [GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra](https://mlsanity.com/paper/2103.03653/) (2021)
- [Deep Learning Library Testing: Definition, Methods and Challenges](https://mlsanity.com/paper/2404.17871/) (2024)
- [An Overview of the Data-Loader Landscape: Comparative Performance Analysis](https://mlsanity.com/paper/2209.13705/) (2022)
- [A stepped sampling method for video detection using LSTM](https://mlsanity.com/paper/2107.08471/) (2021)
- [Baihe: SysML Framework for AI-driven Databases](https://mlsanity.com/paper/2112.14460/) (2021)
- [SAIH: A Scalable Evaluation Methodology for Understanding AI Performance Trend on HPC Systems](https://mlsanity.com/paper/2212.03410/) (2022)

## Abstract (arXiv)

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.