Issue No. 03 - March (2012 vol. 23)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TPDS.2011.189
Guojing Cong , IBM T.J. Watson Research Center, Yorktown Heights
I-Hsin Chung , IBM T.J. Watson Research Center, Yorktown Heights
Hui-Fang Wen , IBM T.J. Watson Research Center, Yorktown Heights
David Klepacki , IBM T.J. Watson Research Center, Yorktown Heights
Hiroki Murata , IBM Research - Tokyo, Japan
Yasushi Negishi , IBM Research - Tokyo, Japan
Takao Moriyama , IBM Research, Yorktown Heights
High productivity is critical in harnessing the power of high-performance computing systems to solve science and engineering problems. It is a challenge to bridge the gap between the hardware complexity and the software limitations. Despite significant progress in programming language, compiler, and performance tools, tuning an application remains largely a manual task, and is done mostly by experts. In this paper, we propose a systematic approach toward automated performance analysis and tuning that we expect to improve the productivity of performance debugging significantly. Our approach seeks to build a framework that facilitates the combination of expert knowledge, compiler techniques, and performance research for performance diagnosis and solution discovery. With our framework, once a diagnosis and tuning strategy has been developed, it can be stored in an open and extensible database and thus be reused in the future. We demonstrate the effectiveness of our approach through the automated performance analysis and tuning of two scientific applications. We show that the tuning process is highly automated, and the performance improvement is significant.
Performance tuning, performance tool.
I. Chung et al., "A Systematic Approach toward Automated Performance Analysis and Tuning," in IEEE Transactions on Parallel & Distributed Systems, vol. 23, no. , pp. 426-435, 2011.