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：过去几年间，稀疏张量代数工作负载的激增催生了大量专用领域加速器。由于稀疏张量中存在不规则性，这些加速器采用了多种新型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的研究尚未涵盖这些完整的设计特性，导致难以理解每种设计选择的影响，也无法对现有最优方案进行比较或扩展。为此，我们提出TeAAL：一种用于精确简洁地描述和评估稀疏张量代数架构的语言及编译器。我们利用TeAAL表示并评估了四种截然不同的最优加速器——ExTensor、Gamma、OuterSPACE和SIGMA——并验证了其能以高精度复现这些加速器的性能。最后，通过展示如何使用TeAAL将Graphicionado的BFS算法加速38倍、SSSP算法加速4.3倍，我们证明了该框架作为新型加速器设计工具的潜力。