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. This has made it difficult to compare or extend the state of the art, and understand the impact of each design choice. To address this gap, we propose TeAAL: a framework that enables 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 using it to propose a novel accelerator for the sparse MTTKRP kernel.

翻译：过去几年，稀疏张量代数工作负载的激增催生了相应的领域专用加速器。由于稀疏张量的不规则性，这些加速器采用了多种新型解决方案以实现高效性能。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全部范围，导致难以比较或扩展现有技术，并理解每个设计选择的影响。为弥补这一空白，我们提出TeAAL：一个能够简洁精确描述和评估稀疏张量代数架构的框架。我们利用TeAAL对四种截然不同的前沿加速器——ExTensor、Gamma、OuterSPACE和SIGMA——进行建模与评估，验证其能以高精度复现这些加速器的性能表现。最后，我们通过使用TeAAL为稀疏MTTKRP内核设计新型加速器，展示了其作为新型加速器设计工具的潜力。