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Layer-Parallel Training with GPU Concurrency of Deep Residual Neural Networks via Nonlinear Multigrid

A Multigrid Full Approximation Storage algorithm for solving Deep Residual Networks is developed to enable neural network parallelized layer-wise training and concurrent computational kernel execution on GPUs. This work demonstrates a 10.2x speedup over traditional layer-wise model parallelism techniques using the same number of compute units.

preprint2020arXivOpen access
Andrew C. KirbySiddharth SamsiMichael JonesAlbert ReutherJeremy KepnerVijay Gadepally
PerformanceMachine LearningDistributed, Parallel, and Cluster Computing
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Open access6 authors3 topics
Imported metadata coverageMissing code, dataset, citation and institution fields are tracked without dominating the paper.Details
Citations: 0Reviews: 0Saves: 0Code: not linkedDataset: not linkedInstitutions: 0

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Andrew C. KirbySiddharth SamsiMichael JonesAlbert ReutherJeremy KepnerVijay Gadepally

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