Write Quick, Run Fast: Sparse Deep Neural Network in 20 Minutes of Development Time via SuiteSparse:GraphBLAS

Abstract

SuiteSparse:GraphBLAS is a full implementation of the GraphBLAS standard, which provides a powerful and expressive framework for creating graph algorithms based on the elegant mathematics of sparse matrix operations on a semiring. Algorithms written in GraphBLAS achieve high performance with minimal development time. Using GraphBLAS, it took a mere 20 minutes to write a first-cut computational kernel that solves the Sparse Deep Neural Network Graph Challenge. Understanding the problem description and file format, writing code to read in the files that define the problem, and comparing our results with the reference solution took a full day. The kernel consists of a single for-loop around 4 lines of code, all of which are calls to GraphBLAS, and it worked perfectly the first time it was compiled. The sequential performance of the GraphBLAS solution is 3x to 5x faster than the MATLAB reference implementation. OpenMP parallelism gives an additional 10x to 15x speedup on a 20-core Intel processor, 17x on an IBM Power8 system, and 20x on a Power9 system, for the largest problems. Since SuiteSparse:GraphBLAS does not yet employ MPI, this was added at the application level, a development effort that took one week, primarily because of difficulties in resolving a load-balancing issue in the MPI-based parallel algorithm.