NDLI: Extracting facts from performance tuning history of scientific applications for predicting effective optimization patterns

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Extracting facts from performance tuning history of scientific applications for predicting effective optimization patterns

Content Provider	ACM Digital Library
Author	Minami, Kazuo Hashimoto, Masatomo Maeda, Toshiyuki Terai, Masaaki
Abstract	To improve performance of large-scale scientific applications, scientists or tuning experts make various empirical attempts to change compiler options, program parameters or even the syntactic structure of programs. Those attempts followed by performance evaluation are repeated until satisfactory results are obtained. The task of performance tuning requires a great deal of time and effort. On account of combinatorial explosion of possible attempts, scientists/tuning experts have a tendency to make decisions on what to be explored just based on their intuition or good sense of tuning. We advocate evidence-based performance tuning (EBT) that facilitates the use of database of facts extracted from tuning histories of applications to guide the exploration of the search space. However, in general, performance tuning is conducted as transient tasks without version control systems. Tuning histories may lack explicit facts about what kind of program transformation contributed to the better performance or even about the chronological order of the source code snapshots. For reconstructing the missing information, we employ a state-of-the-art fine-grained change pattern identification tool for inferring applied transformation patterns only from an unordered set of source code snapshots. The extracted facts are intended to be stored and queried for further data mining. This paper reports on experiments of tuning pattern identification followed by predictive model construction conducted for a few scientific applications tuned for the K supercomputer.
Starting Page	13
Ending Page	23
Page Count	11
File Format	PDF
ISBN	9780769555942
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2015-05-16
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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