MLPerf HPC - DeepCAM

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Date: 2021-10-20

Name: MLPerf HPC - DeepCAM

Domain: Climate & Earth Science

Focus: Scientific ML training and inference on HPC systems

Task Types: Training, Inference

Metrics: Training time, Accuracy, GPU utilization

Models: DeepCAM

AI/ML Motif: Classification

Resources

Benchmark: Visit

Results: Gemini LLM Deep Research

Keywords

HPC training inference scientific ML

Citation

• Steven Farrell, Murali Emani, Jacob Balma, Lukas Drescher, Aleksandr Drozd, Andreas Fink, Geoffrey Fox, David Kanter, Thorsten Kurth, Peter Mattson, Dawei Mu, Amit Ruhela, Kento Sato, Koichi Shirahata, Tsuguchika Tabaru, Aristeidis Tsaris, Jan Balewski, Ben Cumming, Takumi Danjo, Jens Domke, Takaaki Fukai, Naoto Fukumoto, Tatsuya Fukushi, Balazs Gerofi, Takumi Honda, Toshiyuki Imamura, Akihiko Kasagi, Kentaro Kawakami, Shuhei Kudo, Akiyoshi Kuroda, Maxime Martinasso, Satoshi Matsuoka, Henrique Mendonça, Kazuki Minami, Prabhat Ram, Takashi Sawada, Mallikarjun Shankar, Tom St. John, Akihiro Tabuchi, Venkatram Vishwanath, Mohamed Wahib, Masafumi Yamazaki, and Junqi Yin. Mlperf hpc: a holistic benchmark suite for scientific machine learning on hpc systems. 2021. URL: https://arxiv.org/abs/2110.11466, arXiv:2110.11466.

@misc{farrell2021mlperfhpcholisticbenchmark,
  archiveprefix = {arXiv},
  author        = {Steven Farrell and Murali Emani and Jacob Balma and Lukas Drescher and Aleksandr Drozd and Andreas Fink and Geoffrey Fox and David Kanter and Thorsten Kurth and Peter Mattson and Dawei Mu and Amit Ruhela and Kento Sato and Koichi Shirahata and Tsuguchika Tabaru and Aristeidis Tsaris and Jan Balewski and Ben Cumming and Takumi Danjo and Jens Domke and Takaaki Fukai and Naoto Fukumoto and Tatsuya Fukushi and Balazs Gerofi and Takumi Honda and Toshiyuki Imamura and Akihiko Kasagi and Kentaro Kawakami and Shuhei Kudo and Akiyoshi Kuroda and Maxime Martinasso and Satoshi Matsuoka and Henrique Mendonça and Kazuki Minami and Prabhat Ram and Takashi Sawada and Mallikarjun Shankar and Tom St. John and Akihiro Tabuchi and Venkatram Vishwanath and Mohamed Wahib and Masafumi Yamazaki and Junqi Yin},
  eprint        = {2110.11466},
  primaryclass  = {cs.LG},
  title         = {MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems},
  url           = {https://arxiv.org/abs/2110.11466},
  year          = {2021}
}

Ratings

CategoryRating

Software

3.00

Reference implementations exist but containerization and environment setup require manual effort across HPC systems.

Specification

4.00

Hardware constraints and I/O formats are not fully defined for all scenarios.

Dataset

5.00

Not all data is independently versioned or comes with standardized FAIR metadata.

Metrics

5.00

None

Reference Solution

4.00

Reproducibility and environment tuning depend on system configuration; baseline models not uniformly bundled.

Documentation

4.00

Central guidance is available but requires domain-specific effort to replicate results across systems.

Average rating: 4.17/5

Radar plot

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