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arXiv · 2211.03251

Stardust: Compiling Sparse Tensor Algebra to a Reconfigurable Dataflow Architecture

Olivia Hsu·Alexander Rucker·Tian Zhao·Kunle Olukotun·Fredrik Kjolstad
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Abstract

We introduce Stardust, a compiler that compiles sparse tensor algebra to reconfigurable dataflow architectures (RDAs). Stardust introduces new user-provided data representation and scheduling language constructs for mapping to resource-constrained accelerated architectures. Stardust uses the information provided by these constructs to determine on-chip memory placement and to lower to the Capstan RDA through a parallel-patterns rewrite system that targets the Spatial programming model. The Stardust compiler is implemented as a new compilation path inside the TACO open-source system. Using cycle-accurate simulation, we demonstrate that Stardust can generate more Capstan tensor operations than its authors had implemented and that it results in 138$\times$ better performance than generated CPU kernels and 41$\times$ better performance than generated GPU kernels.

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Olivia Hsu, Alexander Rucker, Tian Zhao, Kunle Olukotun, Fredrik Kjolstad. 2022-11-07. Stardust: Compiling Sparse Tensor Algebra to a Reconfigurable Dataflow Architecture. https://arxiv.org/abs/2211.03251

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