Hardware task scheduling optimizations for reconfigurable computing

Abstract

Reconfigurable computers (RC) can provide significant performance improvement for domain applications. However, wide acceptance of todaypsilas RCs among domain scientist is hindered by the complexity of design tools and the required hardware design experience. Recent developments in hardware/software co-design methodologies for these systems provide the ease of use, but they are not comparable in performance to manual co-design. This paper aims at improving the overall performance of hardware tasks assigned to FPGA. Particularly the analysis of inter-task communication as well as data dependencies among tasks are used to reduce the number of configurations and to minimize the communication overhead and task processing time. This work leverages algorithms developed in the RC and reconfigurable hardware (RH) domains to address efficient use of hardware resources to propose two algorithms, weight-based scheduling (WBS) and highest priority first-next fit (HPF-NF). However, traditional resource based scheduling alone is not sufficient to reduce the performance bottleneck, therefore a comprehensive algorithm is necessary. The reduced data movement scheduling (RDMS) algorithm is proposed to address dependency analysis and inter-task communication optimizations. Simulation shows that compared to WBS and HPF-NF, RDMS is able to reduce the amount of FPGA configurations to schedule random generated graphs with heavy weight nodes by 30% and 11% respectively. Additionally, the proof-of-concept implementation of a complex 13-node example task graph on the SGI RC100 reconfigurable computer shows that RDMS is not only able to trim down the amount of necessary configurations from 6 to 4 but also to reduce communication overhead by 48% and the hardware processing time by 33%.