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TSM2X: High-performance tall-and-skinny matrix–matrix multiplication on GPUs.
- Source :
-
Journal of Parallel & Distributed Computing . May2021, Vol. 151, p70-85. 16p. - Publication Year :
- 2021
-
Abstract
- Linear algebra operations have been widely used in big data analytics and scientific computations. Many works have been done on optimizing linear algebra operations on GPUs with regular-shaped input. However, few works focus on fully utilizing GPU resources when the input is not regular-shaped. Current optimizations do not consider fully utilizing the memory bandwidth and computing power; therefore, they can only achieve sub-optimal performance. In this paper, we propose two efficient algorithms – TSM2R and TSM2L – for two classes of tall-and-skinny matrix–matrix multiplications on GPUs. Both of them focus on optimizing linear algebra operation with at least one of the input matrices tall-and-skinny. Specifically, TSM2R is designed for a large regular-shaped matrix multiplying a tall-and-skinny matrix, while TSM2L is designed for a tall-and-skinny matrix multiplying a small regular-shaped matrix. We implement our proposed algorithms and test on several modern NVIDIA GPU micro-architectures. Experiments show that, compared to the current state-of-the-art works, (1) TSM2R speeds up the computation by 1.6x on average and improves the memory bandwidth utilization and computing power utilization by 18.1% and 20.5% on average, respectively, when the regular-shaped matrix size is relatively large or medium; and (2) TSM2L speeds up the computation by 1.9x on average and improves the memory bandwidth utilization by up to 9.3% on average when the regular-shaped matrix size is relatively small. • Few works focus on optimizing GEMM on GPUs for the irregular-shaped input. • Current optimizations do not fully utilize the memory bandwidth and computing power. • We propose two efficient algorithms for two classes of tall-and-skinny GEMM on GPUs. • Our optimizations speedup GEMM by 1.1x ∼ 3.5x for various tall-and-skinny inputs. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 07437315
- Volume :
- 151
- Database :
- Academic Search Index
- Journal :
- Journal of Parallel & Distributed Computing
- Publication Type :
- Academic Journal
- Accession number :
- 149155624
- Full Text :
- https://doi.org/10.1016/j.jpdc.2021.02.013