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 $1.9\times$ on BFS and $1.2\times$ on SSSP over GraphDynS.

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服务于这些任务的领域专用加速器。由于稀疏张量中存在的非规则性，这些加速器采用了多种创新方案以实现高性能。然而，现有关于设计灵活的稀疏加速器建模的研究未能涵盖这些设计特征的完整范围，导致难以理解每种设计选择的影响，也无法对现有技术进行对比或扩展。为解决这一问题，我们提出TeAAL：一种用于简洁精确地描述和评估稀疏张量代数加速器的语言与模拟器生成框架。我们利用TeAAL对四种不同架构的现有先进加速器（ExTensor、Gamma、OuterSPACE和SIGMA）进行建模与评估，验证了其能以高精度复现这些加速器的性能。最后，我们通过展示如何利用TeAAL加速面向顶点编程的加速器（在BFS上实现$1.9\times$加速，在SSSP上实现$1.2\times$加速，性能优于GraphDynS），证明了其作为新型加速器设计工具的潜力。