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× better performance than generated CPU kernels and 41× better performance than generated GPU kernels.

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BibTeX

 @misc{hsu2022stardust, 
      title={Stardust: Compiling Sparse Tensor Algebra to a 
      Reconfigurable Dataflow Architecture}, author={Olivia Hsu and 
      Alexander Rucker and Tian Zhao and Kunle Olukotun and Fredrik Kjolstad}, 
      year={2022}, 
      eprint={2211.03251}, 
      archivePrefix={arXiv}, 
      primaryClass={cs.PL} }