POSTER: An optimization of dataflow architectures for scientific applications

Abstract

Dataflow computing is proved to be promising in high-performance computing. However, traditional dataflow architectures are general-purpose and not efficient enough when dealing with typical scientific applications due to low utilization of function units. In this paper, we propose an optimization of dataflow architectures for scientific applications. The optimization introduces a request for operands mechanism and a topology-based instruction mapping algorithm to improve the efficiency of dataflow architectures. Experimental results show that the request for operands optimization achieves a 4.6% average performance improvement over the traditional dataflow architectures and the TBIM algorithm achieves a 2.28× and a 1.98× average performance improvement over SPDI and SPS algorithm respectively.