A methodology towards automatic performance analysis of parallel applications

Parallel Computing

Volumn 30, Issue 2, 2004, Pages 211-223

  Calzarossa, Maria ^a   Massari, Luisa ^a   Tessera, Daniele ^a  

^a UNIVERSITY OF PAVIA   (Italy)

Author keywords

Load imbalance; Parallel application; Performance analysis; Performance metrics

Indexed keywords

AUTOMATION; COMPUTER DEBUGGING; ITERATIVE METHODS; SOFTWARE PROTOTYPING; TUNING;

LOAD IMBALANCE; PARALLEL APPLICATIONS; PERFORMANCE ANALYSIS; PERFORMANCE METRICS;

PARALLEL PROCESSING SYSTEMS;

EID: 1342306688     PISSN: 01678191     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.parco.2003.08.002     Document Type: Article

References (18)

1. Calzarossa M., Massari L., Merlo A., Pantano M., Tessera D. Medea: a tool for workload characterization of parallel systems. IEEE Parallel and Distributed Technology. 3(4):1995;72-80.
2. L. DeRose, Y. Zhang, D.A. Reed, SvPablo: a multi-language performance analysis system, in: R. Puigjaner, N. Savino, B. Serra (Eds.), Computer Performance Evaluation - Modelling Techniques and Tools, volume 1469 of Lecture Notes in Computer Science, Springer, 1998, pp. 352-355.
3. A. Espinosa, T. Margalef, E. Luque, Automatic performance evaluation of parallel programs, in: Proceedings of 6th Euromicro Workshop on Parallel and Distributed Processing, IEEE Press, 1998, pp. 43-49.
4. T. Fahringer, M. Geissler, G. Madsen, H. Moritsch, C. Seragiotto, On using Aksum for semi-automatically searching of performance problems in parallel and distributed programs, in: Proceedings of 11th Euromicro Workshop on Parallel and Distributed Processing, IEEE Press, 2003, pp. 385-392.
5. K. Ferschweiler, M. Calzarossa, C. Pancake, D. Tessera, D. Keon, A community databank for performance tracefiles, in: Y. Cotronis, J. Dongarra (Eds.), Recent Advances in Parallel Virtual Machine and Message Passing Interface, volume 2131 of Lecture Notes in Computer Science, Springer, 2001, pp. 233-240.
6. Gropp W., Lusk E., Doss N., Skjellum A. High-performance, portable implementation of the MPI Message Passing Interface standard. Parallel Computing. 22(6):1996;789-828.
7. Hartigan J.A. Clustering Algorithms. 1975;J. Wiley.
8. B. Helm, A. Malony, S. Fickas, Capturing and automating performance diagnosis: the Poirot approach, in: Proceedings of the 1995 International Parallel Processing Symposium, 1995, pp. 606-613.
9. K.L. Karavanic, B.P. Miller, Improving online performance diagnosis by the use of historical performance data, in: Proceedings of SC'99, 1999.
10. A. Malagoli, A. Dubey, F. Cattaneo, D. Levine, A portable and efficient parallel algorithm for astrophysical fluid dynamics, in: Parallel Computational Fluid Dynamics, North-Holland, 1995, pp. 553-560.
11. Marshall A.W., Olkin I. Inequalities: Theory of Majorization and Its Applications. 1979;Academic Press.
12. Miller B.P., Callaghan M.D., Cargille J.M., Hollingsworth J.K.H., Irvin R.B., Karavanic K.L., Kunchithapadam K., Newhall T. The Paradyn parallel measurement performance tool. IEEE Computer. 28(11):1995;37-46.
13. NAS Parallel Benchmarks. Available from 〈 http://www.nas.nasa.gov/NAS/NPB 〉.
14. P.C. Roth, B.P. Miller, Deep Start: a hybrid strategy for automated performance problem searches, in: B. Monien, R. Feldman (Eds.), Euro-Par 2002. Parallel Processing, volume 2400 of Lecture Notes in Computer Science, Springer, 2002, pp. 86-96.
15. Shaked M., Shanthikumar J.G. Stochastic Orders and Their Applications. 1994;Academic Press.
16. M.L. Simmons, A.H. Hayes, J.S. Brown, D.A. Reed (Eds.), Debugging and performance tuning for parallel computing systems, IEEE Computer Society, 1996.
17. Yan J.C., Sarukkai S.R. Analyzing parallel program performance using normalized performance indices and trace transformation techniques. Parallel Computing. 22(9):1996;1215-1237.
18. Zaki O., Lusk E., Gropp W., Swider D. Toward scalable performance visualization with jumpshot. The International Journal of High Performance Computing Applications. 13(2):1999;277-288.