Accelerating DNN Inference with GraphBLAS and the GPU

Abstract

This work addresses the 2019 Sparse Deep Neural Network Graph Challenge with an implementation of this challenge using the GraphBLAS programming model. We demonstrate our solution to this challenge with GraphBLAST, a GraphBLAS implementation on the GPU, and compare it to SuiteSparse, a GraphBLAS implementation on the CPU. The GraphBLAST implementation is $1.94 \times $ faster than Suite-Sparse; the primary opportunity to increase performance on the GPU is a higher-performance sparse-matrix-times-sparse-matrix (SpGEMM) kernel.