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：近年来，稀疏张量代数工作负载的激增引发了为服务此类负载的领域专用加速器的相应兴起。由于稀疏张量中存在的不规则性，这些加速器采用了多种新颖的解决方案以实现良好性能。与此同时，先前关于设计灵活的稀疏加速器建模的研究未能完整表达这一系列设计特性，使得理解每个设计选择的影响以及比较或扩展现有先进技术变得困难。为解决此问题，我们提出了TeAAL：一种用于简洁而精确地指定和评估稀疏张量代数加速器的语言及模拟器生成器。我们使用TeAAL来表示和评估四种不同的先进加速器——ExTensor、Gamma、OuterSPACE和SIGMA——并验证了其能以高精度复现它们的性能。最后，我们通过展示如何利用TeAAL来加速以顶点为中心编程的加速器，证明了其作为设计新型加速器工具的潜力——在BFS上实现了相对于GraphDynS的$1.9\times$加速，在SSSP上实现了$1.2\times$加速。