Using information from prior runs to improve automated tuning systems

IEEE/ACM SC2004 Conference, Proceedings

Volumn , Issue , 2004, Pages 123-134

  Chung, I Hsin ^a   Hollingsworth, Jeffrey K ^a  

^a University of Maryland ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACTIVE HARMONY SYSTEMS; RUNTIME SYSTEMS; TUNING SYSTEMS; WEB SERVICES;

AUTOMATION; DATA PROCESSING; HARMONIC ANALYSIS; PARAMETER ESTIMATION; PERFORMANCE;

TUNING;

EID: 23944476799     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (30)

1. The Apache Jakarta Project http://jakarta.apache.org/.
2. DataGen 3.0 http://datasetgenerator.com.
3. MySQL Database Server, MySQL AB http://www.mysql.com.
4. Squid Web Proxy Cache http://www.squid-cache.org/.
5. TPC Benchmark W http://www.tpc.org/tpcw.
6. Anglano, C. Predicting parallel applications performance on non-dedicated cluster platforms, in International conference on Supercomputing. 1998. Melbourne, Australia.
7. Bagrodia, R., et al. Performance prediction of large parallel applications using parallel simulations. in ACM SIGPLAN symposium on Principles and practice of parallel programming. 1999. Atlanta, Georgia, United States.
8. Bezenek, T., et al., Java TPC-W Implementation Distribution http://www.ece.wisc.edu/~pharm/tpcw.shtml.
9. Block, R.J., S. Sarukkai, and P. Mehra. Automated performance prediction of message-passing parallel programs, in ACM/IEEE conference on Supercomputing. 1995. San Diego, California, United States.
10. Brent, R.P., Algorithms for Minimization Without Derivatives. 1973, Englewood Cliffs, NJ: Prentice-Hall.
11. Chen, P.M. and D. A. Patterson, A new approach to I/O performance evaluation: self-scaling I/O benchmarks, predicted I/O performance. ACM Transactions on Computer Systems (TOCS), 1994.12(4): p. 308-339.
12. Chung, I.-H. and J.K. Hollingsworth. Automated Cluster-Based Web Service Performance Tuning, in HPDC-13. 2004. Honolulu, Hawaii, USA.
13. Dantzig, G.B., Linear programming and extensions. 1963, Princeton, N.J.: Princeton University Press.
14. Duda, R.O. and P.E. Hart, Pattern classification and scene analysis. 1973, New York: John Wiley & Sons.
15. Faerman, M., et al. Adaptive performance prediction for distributed data-intensive applications, in ACM/IEEE conference on Supercomputing. 1999. Portland, Oregon, United States.
16. Fahringer, T., Automatic Performance Prediction of Parallel Programs. 1996: Kluwer Academic Publishers, Boston. 296.
17. Hollingsworth, J.K. and P.J. Keleher. Prediction and Adaptation in Active Harmony, in The 7th International Symposium on High Performance Distributed Computing. 1998. Chicago.
18. Jain, R., The Art of Computer Systems Performance Analysis. 1991: John Wiley & Sons, Inc.
19. Jin, R. and G. Agrawal. Performance prediction for random write reductions: a case study in modeling shared memory programs, in ACM SIGMETRICS international conference on Measurement and modeling of computer systems. 2002. Marina Del Rey, California.
20. Kapadia, N.H., J.A.B. Fortes, and C.E. Brodley. Predictive application-performance modeling in a computational grid environment, in The Eighth IEEE Symposium on High Performance Distributed Computing. 1999. Redondo Beach, CA, USA.
21. Keleher, P.J., J.K. Hollingsworth, and D. Perkovic. Exposing Application Alternatives. in ICDCS. 1999. Austin, TX.
22. Lira, C.-C., et al. Performance prediction tools for parallel discrete-event simulation, in workshop on Parallel and distributed simulation. 1999. Atlanta, Georgia, United States.
23. Neider, J.A. and R. Mead, A Simplex Methd for Function Minimization. Comput. J., 1965. 7(4): p. 308-313.
24. Plackert, R.L. and J.P. Burman, The Design of Optimum Multifactorial Experiments. Biometrika, 1946. 33(4): p. 305-325.
25. Saavedra, R.H. and A.J. Smith, Analysis of benchmark characteristics and benchmark performance prediction. ACM Transactions on Computer Systems (TOCS), 1996. 14(4): p. 344-384.
26. Schumann, M. Automatic Performance Prediction to Support Cross Development of Parallel Programs, in SPDT'96: SIGMETRICS Symposium on Parallel and Distributed Tools. 1996. Philadelphia, PA.
27. Simmons, D.M., Linear programming for Operations Research. 1972, San Franscisco: Holden-Day.
28. Snavely, A., et al. A Framework for Application Performance Modeling and Prediction, in Supercomputing 2002. 2002. Baltimore, MD.
29. Tapus, C., I.-H. Chung, and J.K. Hollingsworth. Active Harmony: Towards Automated Performance Tuning, in SC'02. 2002. Baltimore, Maryland.
30. Vraalsen, F., et al. Performance Contracts: Predicting and Monitoring Grid Application Behavior, in Grid Computing - GRID 2001. 2001. Denver, CO.