Limits of Statically-Scheduled Token Dataflow Processing

Abstract

FPGA-based token dataflow processing has been shown to accelerate hard-to-parallelize problems exhibiting irregular dataflow parallelism by as much as an order of magnitude when compared to conventional compute organizations. However, when the structure of the dataflow computation is known upfront, either at compile time or at the start of execution, we can employ static scheduling techniques to further improve performance and enhance compute density of the dataflow hardware. In this paper, we identify the costs and performance trends of both static and dynamic scheduling approaches when considering hardware acceleration of SPICE device equations and Sparse LU factorization in circuit graphs. While the experiments are limited to a case study, the hardware design and dataflow compiler are general and can be extended to other problems and instances where dataflow computing may be applicable. With this study, we hope to develop a quantitative basis for the design of a hybrid dataflow architecture that combines both static and dynamic scheduling techniques. We observe a performance benefit of 2 - 4× and a resource utilization saving of 2 - 3× in favor of statically scheduled hardware.