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：近年来，稀疏张量代数工作负载的激增推动了专用加速器的相应发展。由于稀疏张量存在不规则性，这些加速器采用多种新颖解决方案以实现高性能。然而，现有关于设计灵活的稀疏加速器建模工作未能完整表达此类设计特征的全貌，导致难以理解每个设计选择的影响，也无法对现有最优方案进行比较或扩展。为此，我们提出TeAAL：一种用于精确简洁描述和评估稀疏张量代数架构的语言与编译器。我们通过TeAAL表达并评估了四种迥异的先进加速器——ExTensor、Gamma、OuterSPACE和SIGMA，验证其能高精度复现这些加速器的性能。最后，我们展示了TeAAL作为新型加速器设计工具的潜力：借助该工具，我们可将Graphicionado加速——在广度优先搜索任务上提升38倍，在单源最短路径任务上提升4.3倍。