Parallelization of the SSCA#3 Benchmark on the RAW Processor

Abstract

The MIT Raw machine provides a point-to-point interconnection network for transferring register values between tiles. The programmer schedules the network communication for each tile by himself/herself and guarantees the correctness. It is not easy to parallelize benchmarks by hand for all possible tile configurations on the Raw processor. To overcome this problem, we develop a communication library and a switch code generator to create the switch code for each tile automatically. We implement our techniques for the SSCA#3 (SAR Sensor Processing, Knowledge Formation) benchmark, and evaluate the parallelism on a physical Raw processor. The experimental results show the SSCA#3 benchmark has dense matrix operations with abundant parallelism. Using 16 tiles, the ’SAR image formation’ procedure achieves a speedup of 13.86, and the speedup of the ’object detection’ procedure is 9.98.