NDLI: An idiom-finding tool for increasing productivity of accelerators

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An idiom-finding tool for increasing productivity of accelerators

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An idiom-finding tool for increasing productivity of accelerators

Content Provider	ACM Digital Library
Author	Laurenzano, Michael Olschanowsky, Catherine Snavely, Allan Carrington, Laura Tikir, Mustafa M. Poole, Stephen Peraza, Joshua
Abstract	Suppose one is considering purchase of a computer equipped with accelerators. Or suppose one has access to such a computer and is considering porting code to take advantage of the accelerators. Is there a reason to suppose the purchase cost or programmer effort will be worth it? It would be nice to able to estimate the expected improvements in advance of paying money or time. We exhibit an analytical framework and tool-set for providing such estimates: the tools first look for user-defined idioms that are patterns of computation and data access identified in advance as possibly being able to benefit from accelerator hardware. A performance model is then applied to estimate how much faster these idioms would be if they were ported and run on the accelerators, and a recommendation is made as to whether or not each idiom is worth the porting effort to put them on the accelerator and an estimate is provided of what the overall application speedup would be if this were done. As a proof-of-concept we focus our investigations on Gather/Scatter (G/S) operations and means to accelerate these available on the Convey HC-1 which has a special-purpose "personality" for accelerating G/S. We test the methodology on two large-scale HPC applications. The idiom recognizer tool saves weeks of programmer effort compared to having the programmer examine the code visually looking for idioms; performance models save yet more time by rank-ordering the best candidates for porting; and the performance models are accurate, predicting G/S runtime speedup resulting from porting to within 10% of speedup actually achieved. The G/S hardware on the Convey sped up these operations 20x, and the overall impact on total application runtime was to improve it by as much as 21%.
Starting Page	202
Ending Page	212
Page Count	11
File Format	PDF
ISBN	9781450301022
DOI	10.1145/1995896.1995928
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-05-31
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Fpgas Accelerators Performance modeling Performance prediction Benchmarking Hpc
Content Type	Text
Resource Type	Article

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