TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators
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 th...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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ACM|56th Annual IEEE/ACM International Symposium on Microarchitecture
2024
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| Online Access: | https://hdl.handle.net/1721.1/153258 |
| _version_ | 1826191153874075648 |
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| author | Nayak, Nandeeka Odemuyiwa, Toluwanimi O. Ugare, Shubham Fletcher, Christopher Pellauer, Michael Emer, Joel |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Nayak, Nandeeka Odemuyiwa, Toluwanimi O. Ugare, Shubham Fletcher, Christopher Pellauer, Michael Emer, Joel |
| author_sort | Nayak, Nandeeka |
| collection | MIT |
| description | 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 × on BFS and 1.2 × on SSSP over GraphDynS. |
| first_indexed | 2024-09-23T08:51:39Z |
| format | Article |
| id | mit-1721.1/153258 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:51:39Z |
| publishDate | 2024 |
| publisher | ACM|56th Annual IEEE/ACM International Symposium on Microarchitecture |
| record_format | dspace |
| spelling | mit-1721.1/1532582024-01-29T15:42:53Z TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators Nayak, Nandeeka Odemuyiwa, Toluwanimi O. Ugare, Shubham Fletcher, Christopher Pellauer, Michael Emer, Joel Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory 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 × on BFS and 1.2 × on SSSP over GraphDynS. 2024-01-02T15:30:39Z 2024-01-02T15:30:39Z 2023-10-28 2024-01-01T08:48:48Z Article http://purl.org/eprint/type/ConferencePaper 979-8-4007-0329-4 https://hdl.handle.net/1721.1/153258 Nayak, Nandeeka, Odemuyiwa, Toluwanimi O., Ugare, Shubham, Fletcher, Christopher, Pellauer, Michael et al. 2023. "TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators." PUBLISHER_POLICY en https://doi.org/10.1145/3613424.3623791 Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. The author(s) application/pdf ACM|56th Annual IEEE/ACM International Symposium on Microarchitecture |
| spellingShingle | Nayak, Nandeeka Odemuyiwa, Toluwanimi O. Ugare, Shubham Fletcher, Christopher Pellauer, Michael Emer, Joel TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators |
| title | TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators |
| title_full | TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators |
| title_fullStr | TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators |
| title_full_unstemmed | TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators |
| title_short | TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators |
| title_sort | teaal a declarative framework for modeling sparse tensor accelerators |
| url | https://hdl.handle.net/1721.1/153258 |
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