1 research outputs found
TeAAL: A Declarative Framework for Modeling Sparse Tensor Accelerators
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 on BFS and
on SSSP.Comment: 14 pages, 12 figure