Title :
Autotuning Wavefront Abstractions for Heterogeneous Architectures
Author :
Mohanty, Siddharth ; Cole, Murray
Author_Institution :
Inst. for Comput. Syst. Archit., Univ. of Edinburgh, Edinburgh, UK
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.
Keywords :
learning (artificial intelligence); parallel programming; automated machine learning; autotuning wavefront abstractions; heterogeneous architectures; parallel programming; wave front pattern; Graphics processing units; Kernel; Parallel processing; Support vector machines; Tiles; Tuners; Abstractions; Autotuning; GPU; Heterogeneous Computing; Wavefront;
Conference_Titel :
Applications for Multi-Core Architectures (WAMCA), 2012 Third Workshop on
Conference_Location :
New York, NY
Print_ISBN :
978-1-4673-5025-9
DOI :
10.1109/WAMCA.2012.14
