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 compiler for the concise and precise specification and evaluation of sparse tensor algebra architectures. 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 Graphicionado--by $38\times$ on BFS and $4.3\times$ on SSSP.

翻译：过去几年中，稀疏张量代数工作负载的激增催生了大量专用加速器。由于稀疏张量存在不规则性，这些加速器采用多种新颖方案以实现高性能。然而，现有面向设计灵活的稀疏加速器建模工作未能涵盖所有这些设计特征，导致难以理解各设计选择的影响，也难以比较或扩展现有技术。为此，我们提出TeAAL：一种用于精确规范与评估稀疏张量代数架构的语言与编译器。我们利用TeAAL表示并评估了四种截然不同的先进加速器——ExTensor、Gamma、OuterSPACE和SIGMA——验证了其能以高精度复现加速器性能。最后，我们通过展示TeAAL如何加速Graphicionado（BFS提升38倍，SSSP提升4.3倍），证明了其作为新型加速器设计工具的潜力。