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TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators

Author(s)
Nayak, Nandeeka; Odemuyiwa, Toluwanimi O.; Ugare, Shubham; Fletcher, Christopher; Pellauer, Michael; Emer, Joel; ... Show more Show less
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Abstract
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.
Date issued
2023-10-28
URI
https://hdl.handle.net/1721.1/153258
Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Publisher
ACM|56th Annual IEEE/ACM International Symposium on Microarchitecture
Citation
Nayak, Nandeeka, Odemuyiwa, Toluwanimi O., Ugare, Shubham, Fletcher, Christopher, Pellauer, Michael et al. 2023. "TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators."
Version: Final published version
ISBN
979-8-4007-0329-4

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