Accelerating Graph Neural Networks with a Novel Matrix Compression Format

TL;DR

This paper introduces a novel matrix compression format for sparse adjacency matrices in GNNs, enabling faster matrix multiplications and significantly accelerating GNN inference and training.

Contribution

The paper proposes the Compressed Binary Matrix (CBM) format and efficient kernels that outperform existing methods, accelerating GNN computations.

Findings

Achieves up to 5x speedup in matrix multiplication.

Accelerates GNN inference time by up to 3x.

Outperforms Intel MKL in sparse matrix operations.

Abstract

The inference and training stages of Graph Neural Networks (GNNs) are often dominated by the time required to compute a long sequence of matrix multiplications between the sparse graph adjacency matrix and its embedding. To accelerate these stages, we first propose the Compressed Binary Matrix (CBM) storage format to succinctly represent the binary adjacency matrix of an unweighted graph. Then, we show how to generalize this representation to normalized adjacency matrices of unweighted graphs which arise in the context of GNNs. Finally, we develop efficient matrix multiplication kernels based on this compressed representation. The matrix multiplication kernels proposed in this work never require more scalar operations than classic sparse matrix multiplication algorithms. Experimental evaluation shows that the matrix multiplication strategies proposed outperform the current state-of-the-art implementations provided by Intel MKL, achieving speedups close to 5$\times$. Furthermore, our optimized matrix-multiplication strategies accelerated the inference time of a GNN by up to $3\times$.