TY - GEN
T1 - A comparison of search heuristics for empirical code optimization
AU - Seymour, Keith
AU - You, Haihang
AU - Dongarra, Jack
PY - 2008
Y1 - 2008
N2 - This paper describes the application of various search techniques to the problem of automatic empirical code optimization. The search process is a critical aspect of auto-tuning systems because the large size of the search space and the cost of evaluating the candidate implementations makes it infeasible to find the true optimum point by brute force. We evaluate the effectiveness of Nelder-Mead Simplex, Genetic Algorithms, Simulated Annealing, Particle Swarm Optimization, Orthogonal search, and Random search in terms of the performance of the best candidate found under varying time limits.
AB - This paper describes the application of various search techniques to the problem of automatic empirical code optimization. The search process is a critical aspect of auto-tuning systems because the large size of the search space and the cost of evaluating the candidate implementations makes it infeasible to find the true optimum point by brute force. We evaluate the effectiveness of Nelder-Mead Simplex, Genetic Algorithms, Simulated Annealing, Particle Swarm Optimization, Orthogonal search, and Random search in terms of the performance of the best candidate found under varying time limits.
UR - http://www.scopus.com/inward/record.url?scp=57949106903&partnerID=8YFLogxK
U2 - 10.1109/CLUSTR.2008.4663803
DO - 10.1109/CLUSTR.2008.4663803
M3 - Conference contribution
AN - SCOPUS:57949106903
SN - 9781424426409
T3 - Proceedings - IEEE International Conference on Cluster Computing, ICCC
SP - 421
EP - 429
BT - Proceedings of the 2008 IEEE International Conference on Cluster Computing, CCGRID 2008
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2008 IEEE International Conference on Cluster Computing, ICCC 2008
Y2 - 29 September 2008 through 1 October 2008
ER -