Review paper
A high-performance batched matrix multiplication framework for GPUs under unbalanced input distribution
Abstract
In the past few decades, general matrix multiplication (GEMM), as the basic component of the Basic Linear Algebra Subprograms (BLAS) library, has played a vital role in various fields such as machine learning, image processing, and fluid dynamics. Because these fields tend to deconstruct the problem into multiple smaller sub-problems, today’s BLAS libraries have implemented batched GEMM routines to achieve high performance in this scenario....
Paper Details
Title
A high-performance batched matrix multiplication framework for GPUs under unbalanced input distribution
Published Date
Jun 21, 2021
Volume
78
Issue
2
Pages
1741 - 1758
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