TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators
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3613424.3623791.pdf
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2.54 MB
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Adobe PDF
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Author(s)
Nayak, Nandeeka
Odemuyiwa, Toluwanimi O.
Ugare, Shubham
Fletcher, Christopher
Pellauer, Michael
Emer, Joel
Date Issued
October 28, 2023
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
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.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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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.
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DOI of Published Version
https://doi.org/10.1145/3613424.3623791
