Cosmoflow

The CosmoFlow training application benchmark from the MLPerf HPC v0.5 benchmark suite. It involves training a 3D convolutional neural network on N-body cosmology simulation data to predict physical parameters of the universe.

Metadata

Model cosmoflow.json

Datasets

cosmoUniverse_2019_05_4parE_tf_v2.json

cosmoUniverse_2019_05_4parE_tf_v2_mini.json

Overview

This application is based on the original CosmoFlow paper presented at SC18 and continued by the ExaLearn project, and adopted as a benchmark in the MLPerf HPC suite. It involves training a 3D convolutional neural network on N-body cosmology simulation data to predict physical parameters of the universe. The reference implementation for MLPerf HPC v0.5 CosmoFlow uses TensorFlow with the Keras API and Horovod for data-parallel distributed training. The dataset comes from simulations run by ExaLearn, with universe volumes split into cubes of size 128x128x128 with 4 redshift bins. The total dataset volume preprocessed for MLPerf HPC v0.5 in TFRecord format is 5.1 TB. The target objective in MLPerf HPC v0.5 is to train the model to a validation mean-average-error < 0.124. However, the problem size can be scaled down and the training throughput can be used as the primary objective for a small scale or shorter timescale benchmark.123

Figure 1: Example simulation of dark matter in the universe used as input to the CosmoFlow network. Copied from [NERSC](https://www.nersc.gov/news-publications/nersc-news/science-news/2018/nersc-intel-cray-harness-the-power-of-deep-learning-to-better-understand-the-universe/)

References

  1. https://proxyapps.exascaleproject.org/app/mlperf-cosmoflow/ ↩︎

  2. https://github.com/sparticlesteve/cosmoflow-benchmark ↩︎

  3. https://github.com/sparticlesteve/cosmoflow-benchmark/blob/master/README.md ↩︎

Last modified March 15, 2025: add images (0748a1d)