TY - GEN
T1 - An experimental study of global and local search algorithms in empirical performance tuning
AU - Balaprakash, Prasanna
AU - Wild, Stefan M.
AU - Hovland, Paul D.
PY - 2013
Y1 - 2013
N2 - The increasing complexity, heterogeneity, and rapid evolution of modern computer architectures present obstacles for achieving high performance of scientific codes on different machines. Empirical performance tuning is a viable approach to obtain high-performing code variants based on their measured performance on the target machine. In previous work, we formulated the search for the best code variant as a numerical optimization problem. Two classes of algorithms are available to tackle this problem: global and local algorithms. We present an experimental study of some global and local search algorithms on a number of problems from the recently introduced SPAPT test suite. We show that local search algorithms are particularly attractive, where finding high-preforming code variants in a short computation time is crucial.
AB - The increasing complexity, heterogeneity, and rapid evolution of modern computer architectures present obstacles for achieving high performance of scientific codes on different machines. Empirical performance tuning is a viable approach to obtain high-performing code variants based on their measured performance on the target machine. In previous work, we formulated the search for the best code variant as a numerical optimization problem. Two classes of algorithms are available to tackle this problem: global and local algorithms. We present an experimental study of some global and local search algorithms on a number of problems from the recently introduced SPAPT test suite. We show that local search algorithms are particularly attractive, where finding high-preforming code variants in a short computation time is crucial.
KW - automatic performance tuning
KW - black-box optimization
KW - search
UR - http://www.scopus.com/inward/record.url?scp=84883288790&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-38718-0_26
DO - 10.1007/978-3-642-38718-0_26
M3 - Conference contribution
AN - SCOPUS:84883288790
SN - 9783642387173
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 261
EP - 269
BT - High Performance Computing for Computational Science, VECPAR 2012 - 10th International Conference, Revised Selected Papers
T2 - 10th International Conference on High Performance Computing for Computational Science, VECPAR 2012
Y2 - 17 July 2012 through 20 July 2012
ER -