论文标题
星尘：将稀疏张量代数编译成可重构的数据流架构
Stardust: Compiling Sparse Tensor Algebra to a Reconfigurable Dataflow Architecture
论文作者
论文摘要
我们介绍了Stardust，这是一个编译器，该编译器编译稀疏张量代数以重新配置数据流架构（RDAS）。 Stardust引入了新的用户提供的数据表示和调度语言构造，以映射到资源受限的加速体系结构。星尘使用这些构造提供的信息来确定片上内存放置，并通过针对空间编程模型的平行模式重写系统，以将其降低到Capstan RDA。星尘编译器被实现为炸玉米饼开源系统内部的新编译路径。使用自行车精确的模拟，我们证明，星尘可以产生比作者实施更多的CAPSTAN张量操作，并且比生成的CPU内核更好的性能要比生成的GPU内核更好。
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.
