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Machine Learning benchmarking at NERSC

NERSC uses both standard framework-oriented benchmarks as well as scientific benchmarks from research projects in order to characterize our systems for scientific Deep Learning.

Framework benchmarks


We run a version of the tf_cnn_benchmarks repository as well as a DCGAN model on Cori.

Training results Training results


We have a repository of benchmarks with standard computer vision models, LSTM, and 3D convolutional models here:

Training results Training results

Inference results Inference results

Scientific Deep Learning Benchmarks


The HEP-CNN benchmark trains a simple Convolutional Neural Network to classify LHC collision detector images as signal or background.


The CosmoFlow benchmark trains a 3D Convolutional Neural Network to predict cosmological parameters from simulated universe volumes.


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