利用新型矩阵压缩格式加速图神经网络
Accelerating Graph Neural Networks with a Novel Matrix Compression Format
AI总结：
本文提出压缩二进制矩阵（CBM）格式表示GNN中的邻接矩阵，并基于该格式开发高效矩阵乘法内核，实验表明相比Intel MKL实现可加速近5倍，GNN推理加速达3倍。
摘要：
图神经网络（GNN）的推理和训练阶段通常由计算稀疏图邻接矩阵与其嵌入之间长序列矩阵乘法所需的时间主导。为了加速这些阶段，我们首先提出压缩二进制矩阵（CBM）存储格式，以简洁地表示无权图的二进制邻接矩阵。然后，我们展示如何将这种表示推广到GNN背景下出现的无权图归一化邻接矩阵。最后，我们基于这种压缩表示开发了高效的矩阵乘法内核。本工作提出的矩阵乘法内核所需的标量运算次数从未超过经典稀疏矩阵乘法算法。实验评估表明，所提出的矩阵乘法策略优于Intel MKL提供的当前最先进实现，实现了接近5倍的加速。此外，我们优化的矩阵乘法策略将GNN的推理时间加速了高达3倍。
英文摘要：
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 $3\times$.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
