Autotuning Wavefront Abstractions for Heterogeneous Architectures

Abstract

We present our auto tuned heterogeneous parallel programming abstraction for the wave front pattern. An exhaustive search of the tuning space indicates that correct setting of tuning factors can average 37x speedup over a sequential baseline. Our best automated machine learning based heuristic obtains 92% of this ideal speedup, averaged across our full range of wave front examples.