Sparse GPU Kernels for Deep Learning

TL;DR

This paper develops high-performance GPU kernels for sparse matrix operations in deep learning, enabling significant speedups and memory savings in models like Transformer and MobileNet.

Contribution

The work introduces novel GPU kernels optimized for sparsity in deep learning, achieving practical speedups on Nvidia V100 GPUs.

Findings

Kernels reach 27% of single-precision peak performance.

Sparse models achieve 1.2-2.1x speedups.

Memory savings up to 12.8x without accuracy loss.

Abstract

Scientific workloads have traditionally exploited high levels of sparsity to accelerate computation and reduce memory requirements. While deep neural networks can be made sparse, achieving practical speedups on GPUs is difficult because these applications have relatively moderate levels of sparsity that are not sufficient for existing sparse kernels to outperform their dense counterparts. In this work, we study sparse matrices from deep learning applications and identify favorable properties that can be exploited to accelerate computation. Based on these insights, we develop high-performance GPU kernels for two sparse matrix operations widely applicable in neural networks: sparse matrix-dense matrix multiplication and sampled dense-dense matrix multiplication. Our kernels reach 27% of single-precision peak on Nvidia V100 GPUs. Using our kernels, we demonstrate sparse Transformer and MobileNet models that achieve 1.2-2.1x speedups and up to 12.8x memory savings without sacrificing accuracy.