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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2304.07931 |
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| _version_ | 1866910482476040192 |
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| author | Nayak, Nandeeka Odemuyiwa, Toluwanimi O. Ugare, Shubham Fletcher, Christopher W. Pellauer, Michael Emer, Joel S. |
| author_facet | Nayak, Nandeeka Odemuyiwa, Toluwanimi O. Ugare, Shubham Fletcher, Christopher W. Pellauer, Michael Emer, Joel S. |
| contents | Over the past few years, the explosion in sparse tensor algebra workloads has led to a corresponding rise in domain-specific accelerators to service them. Due to the irregularity present in sparse tensors, these accelerators employ a wide variety of novel solutions to achieve good performance. At the same time, prior work on design-flexible sparse accelerator modeling does not express this full range of design features, making it difficult to understand the impact of each design choice and compare or extend the state-of-the-art.
To address this, we propose TeAAL: a language and simulator generator for the concise and precise specification and evaluation of sparse tensor algebra accelerators. We use TeAAL to represent and evaluate four disparate state-of-the-art accelerators -- ExTensor, Gamma, OuterSPACE, and SIGMA -- and verify that it reproduces their performance with high accuracy. Finally, we demonstrate the potential of TeAAL as a tool for designing new accelerators by showing how it can be used to speed up vertex-centric programming accelerators -- achieving $1.9\times$ on BFS and $1.2\times$ on SSSP over GraphDynS. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_07931 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators Nayak, Nandeeka Odemuyiwa, Toluwanimi O. Ugare, Shubham Fletcher, Christopher W. Pellauer, Michael Emer, Joel S. Hardware Architecture Over the past few years, the explosion in sparse tensor algebra workloads has led to a corresponding rise in domain-specific accelerators to service them. Due to the irregularity present in sparse tensors, these accelerators employ a wide variety of novel solutions to achieve good performance. At the same time, prior work on design-flexible sparse accelerator modeling does not express this full range of design features, making it difficult to understand the impact of each design choice and compare or extend the state-of-the-art. To address this, we propose TeAAL: a language and simulator generator for the concise and precise specification and evaluation of sparse tensor algebra accelerators. We use TeAAL to represent and evaluate four disparate state-of-the-art accelerators -- ExTensor, Gamma, OuterSPACE, and SIGMA -- and verify that it reproduces their performance with high accuracy. Finally, we demonstrate the potential of TeAAL as a tool for designing new accelerators by showing how it can be used to speed up vertex-centric programming accelerators -- achieving $1.9\times$ on BFS and $1.2\times$ on SSSP over GraphDynS. |
| title | TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators |
| topic | Hardware Architecture |
| url | https://arxiv.org/abs/2304.07931 |