Abstract
Conventional performance environments are based on profiling and event instrumentation. It becomes problematic as parallel systems scale to hundreds of nodes and beyond. A framework of developing an integrated performance modeling and prediction system, SCALability Analyzer (SCALA), is presented in this study. In contrast to existing performance tools, the program performance model generated by SCALA is based on scalability analysis. SCALA assumes the availability of modern compiler technology, adopts statistical methodologies, and has the support of browser interface. These technologies, together with a new approach of scalability analysis, enable SCALA to provide the user with a higher and more intuitive level of performance analysis. A prototype SCALA system has been implemented. Initial experimental results show that SCALA is unique in its ability of revealing the scaling properties of a computing system.
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References
Adve, V. S., Crummey, J. M., Anderson, M., Kennedy, K., Wang, J.-C., and Reed, D. A. Integrating complication and performance analysis for data parallel programs. In Proc. of Workshop on Debugging and Performance Tuning for Parallel Computing Systems (Jan. 1996).
Benkner, S., Sanjari, K., Sipkova, V., and Velkov, B. Parallelizing ierregular applications with vienna HPF+compiler VFC. In HPCN Europe (April 1998), Lecture Notes in Computer Science, Springer-Verlag.
Calzarossa, M., Massari, L., Merlo, A., Pantano, M., and Tessera, D. Medea: A tool for workload characterization of parallel systems. IEEE Parallel & Distributed Technology Winter (1995), 72–80.
Calzarossa, M., Massari, L., Merlo, A., Pantano, M., and Tessera, D. Integration of a complication system and a performance tool: The HPF+ approach. In LNCS-HPCN98 (Amsterdam, NL, 1998).
Fahringer, T.Automatic Performance Prediction of Parallel Programs, Kluwer Academic Publishers, Boston, USA, ISBN 0-7923-9708-8, March 1996.
Fox, G., Hiranandani, S., Kennedy, K., Koelbel, C., Kremer, U., Tseng, C., and Wu, M. Fortran D language specification. Technical Report, COMP TR90079, Department of Computer Science, Rice University, Mar. 1991.
Gustafson, J., Montry, G., and Benner, R. Development of parallel methods for a 1024-processor hypercube. SIAM J. of Sci. and Stat. Computing 9, 4 (July 1988), 609–638.
Hwang, K., and Xu, Z.Scalable Parallel Computing. McGraw-Hill WCB, 1998.
Kumar, V., Grama, A., Gupta, A., and Karypis, G.Introduction to Parallel Computing, Design and Analysis of Algorithms. The Benjamin/Cummings Publishing Company, Inc., 1994.
Lyon, G., Snelick, R., and Kacker, R. Synthetic-perturbation tuning of mimd programs. Journal of Supercomputing 8, 1 (1994), 5–8.
Noelle, M., Pantano, M., and Sun, X.-H. Communication overhead: Prediction and its influence on scalability. In Proc. the International Conference on Parallel and Distributed Processing Techniques and Applications (July 1998).
Reed, D., Aydt, R., Madhyastha, T., Noe, R., Shields, K., and Schwartz, B. An overview of the Pablo performance analysis environment. In Technical Report. UIUCCS, Nov. 1992.
Sahni, S., and Thanvantri, V. Performance metrics: Keeping the focus on runtime. IEEE Parallel & Distributed Technology (Spring 1996), 43–56.
Sun, X.-H. The relation of scalability and execution time. In Proc. of the International Parallel Processing Symposium’96 (April 1996).
Sun, X.-H. Scalability versus execution time in scalable systems. TR-97-003 (Revised May 1998), Louisiana State University, Department of Computer Science, 1997.
Sun, X.-H. Performance range comparison via crossing point analysis. In Lecture Notes in Computer Science, No 1388, J. Rolim, Ed. Springer, March 1998. Parallel and Distributed Processing.
Sun, X.-H., He, D., Cameron, K., and Luo, Y. A factorial performance evaluation for hierarchical memory systems. In Proc. of the IEEE Int’l Parallel Processing Symposium (Apr. 1999).
Sun, X.-H., Pantano, M., and Fahringer, T. Integrated range comparison for data-parallel compilation systems. IEEE Transactions on Parallel and Distributed Systems (accepted to appear, 1999).
Sun, X.-H., and Rover, D. Scalability of parallel algorithm-machine combinations. IEEE Transactions on Parallel and Distributed Systems (June 1994), 599–613.
SUN Microsystems Inc. Java 3D API specification. http://java.sun.com/products/java-media/3D, 1998.
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Sun, XH., Pantano, M., Fahringer, T., Zhan, Z. (1999). SCALA: A framework for performance evaluation of scalable computing. In: Rolim, J., et al. Parallel and Distributed Processing. IPPS 1999. Lecture Notes in Computer Science, vol 1586. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0097887
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DOI: https://doi.org/10.1007/BFb0097887
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