Fast Kronecker Matrix-Matrix Multiplication on GPUs

Abhinav Jangda, Mohit Yadav
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Abstract

Kronecker Matrix-Matrix Multiplication (Kron-Matmul) is the multiplication of a matrix with the Kronecker Product of several smaller matrices. Kron-Matmul is a core operation for many scientific and machine learning computations. State-of-the-art Kron-Matmul implementations utilize existing tensor algebra operations, such as matrix multiplication, transpose, and tensor matrix multiplication. However, this design choice prevents several Kron-Matmul specific optimizations, thus, leaving significant performance on the table. To address this issue, we present FastKron, an efficient technique for Kron-Matmul on single and multiple GPUs. FastKron is independent of linear algebra operations enabling several new optimizations for Kron-Matmul. Thus, it performs up to 40.7x and 7.85x faster than existing implementations on 1 and 16 GPUs respectively.
GPU 上的快速 Kronecker 矩阵-矩阵乘法
克罗内克矩阵-矩阵乘法(Kron-Matmul)是将一个矩阵与几个较小矩阵的克罗内克乘积相乘。Kron-Matmul 是许多科学和机器学习计算的核心运算。最先进的 Kron-Matmul 实现利用了现有的张量代数运算,如矩阵乘法、转置和张量矩阵乘法。然而,这种设计选择阻碍了几种 Kron-Matmul 特定的优化,从而使性能大打折扣。为了解决这个问题,我们提出了在单个和多个 GPU 上实现 Kron-Matmul 的高效技术 FastKron。FastKron 独立于线性代数运算,可对 Kron-Matmul 进行多项新的优化。因此,在 1 个和 16 个 GPU 上,它的性能分别比现有实现快 40.7 倍和 7.85 倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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