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

Abstract

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.

7 pages, 6 figures, 27 citations. Accepted to 2020 IEEE High Performance Extreme Computing Conference - Outstanding Paper Award