Accelerating Sparse MTTKRP for Small Tensor Decomposition on GPU
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866908280326979584 |
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| author | Wijeratne, Sasindu Kannan, Rajgopal Prasanna, Viktor |
| author_facet | Wijeratne, Sasindu Kannan, Rajgopal Prasanna, Viktor |
| contents | Sparse Matricized Tensor Times Khatri-Rao Product (spMTTKRP) is the bottleneck kernel of sparse tensor decomposition. In tensor decomposition, spMTTKRP is performed iteratively along all the modes of an input tensor. In this work, we propose a mode-specific tensor layout on GPU that uses multiple tensor copies, where each copy is optimized for a specific mode. The proposed tensor layout increases the data locality of external memory accesses and eliminates the intermediate values communicated between the GPU thread blocks and the GPU global memory. We also propose a tensor partitioning scheme to optimally distribute the total computations among GPU streaming multiprocessors based on the sparsity and the dimensions of the input tensor. Our approach achieves a geometric mean speedup of 2.4x, 7.9x, and 8.9x in total execution time compared with the state-of-the-art GPU baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_18198 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accelerating Sparse MTTKRP for Small Tensor Decomposition on GPU Wijeratne, Sasindu Kannan, Rajgopal Prasanna, Viktor Distributed, Parallel, and Cluster Computing Sparse Matricized Tensor Times Khatri-Rao Product (spMTTKRP) is the bottleneck kernel of sparse tensor decomposition. In tensor decomposition, spMTTKRP is performed iteratively along all the modes of an input tensor. In this work, we propose a mode-specific tensor layout on GPU that uses multiple tensor copies, where each copy is optimized for a specific mode. The proposed tensor layout increases the data locality of external memory accesses and eliminates the intermediate values communicated between the GPU thread blocks and the GPU global memory. We also propose a tensor partitioning scheme to optimally distribute the total computations among GPU streaming multiprocessors based on the sparsity and the dimensions of the input tensor. Our approach achieves a geometric mean speedup of 2.4x, 7.9x, and 8.9x in total execution time compared with the state-of-the-art GPU baselines. |
| title | Accelerating Sparse MTTKRP for Small Tensor Decomposition on GPU |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2503.18198 |