TY - GEN
T1 - Towards a framework for automated performance tuning
AU - Cong, G.
AU - Seelam, S.
AU - Chung, I.
AU - Wen, H.
AU - Klepacki, D.
PY - 2009
Y1 - 2009
N2 - As part of the DARPA sponsored High Productivity Computing Systems (HPCS) program, IBM is building petaflop supercomputers that will be fast, powerefficient, and easy to program. In addition to high performance, high productivity to the end user is another prominent goal. The challenge is to develop technologies that bridge the productivity gap - the gap between the hardware complexity and the software limitations. In addition to language, compiler, and runtime research, powerful and user-friendly performance tools are critical in debugging performance problems and tuning for maximum performance. Traditional tools have either focused on specific performance aspects (e.g., communication problems) or provided limited diagnostic capabilities, and using them alone usually do not pinpoint accurately performance problems. Even fewer tools attempt to provide solutions for problems detected. In our study, we develop an open framework that unifies tools, compiler analysis, and expert knowledge to automatically analyze and tune the performance of an application. Preliminary results demonstrated the efficiency of our approach.
AB - As part of the DARPA sponsored High Productivity Computing Systems (HPCS) program, IBM is building petaflop supercomputers that will be fast, powerefficient, and easy to program. In addition to high performance, high productivity to the end user is another prominent goal. The challenge is to develop technologies that bridge the productivity gap - the gap between the hardware complexity and the software limitations. In addition to language, compiler, and runtime research, powerful and user-friendly performance tools are critical in debugging performance problems and tuning for maximum performance. Traditional tools have either focused on specific performance aspects (e.g., communication problems) or provided limited diagnostic capabilities, and using them alone usually do not pinpoint accurately performance problems. Even fewer tools attempt to provide solutions for problems detected. In our study, we develop an open framework that unifies tools, compiler analysis, and expert knowledge to automatically analyze and tune the performance of an application. Preliminary results demonstrated the efficiency of our approach.
UR - http://www.scopus.com/inward/record.url?scp=70449848129&partnerID=8YFLogxK
U2 - 10.1109/IPDPS.2009.5161157
DO - 10.1109/IPDPS.2009.5161157
M3 - Conference contribution
AN - SCOPUS:70449848129
SN - 9781424437504
T3 - IPDPS 2009 - Proceedings of the 2009 IEEE International Parallel and Distributed Processing Symposium
BT - IPDPS 2009 - Proceedings of the 2009 IEEE International Parallel and Distributed Processing Symposium
T2 - 23rd IEEE International Parallel and Distributed Processing Symposium, IPDPS 2009
Y2 - 23 May 2009 through 29 May 2009
ER -