TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators
56TH IEEE/ACM INTERNATIONAL SYMPOSIUM ON MICROARCHITECTURE, MICRO 2023(2023)
摘要
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-ofthe-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 1.9x on BFS and 1.2x on SSSP over GraphDynS.
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关键词
Sparse Tensor,Acceleration Model,Specific Design,Points In Space,Rank Order,Local Time,Sparse Data,Matrix Multiplication,Local Space,Levels Of Hierarchy,Uncompressed,Loop Order,Graph Algorithms,Input Tensor,Synthetic Matrix,1D Convolution,Domain-specific Languages,Running Example,Einstein Summation,Output Tensor,Concrete Representations,Hardware Parameters,Partial Products,Tensor Form
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