PDRS: A Performance Data Representation System
We present the design and development of a Performance Data Representation System (PDRS) for scalable parallel computing. PDRS provides decision support that helps users find the right data to understand their programs’ performance and to select appropriate ways to display and analyze it. PDRS is an attempt to provide appropriate assistant to help programmers identifying performance bottlenecks and optimizing their programs.
KeywordsPerformance Information Performance Measurement System Performance Bottleneck Performance Database Performance Visualization
Unable to display preview. Download preview PDF.
- 1.R. Aydt, The Pablo Self-Defining Data Format, Department of Computer Science, University of Illinois, April 1995, ftp://bugle.cs.uiuc.edu/pub/Release/Documentation/SDDF.ps.
- 2.T. Fahringer and B. Scholz, Symbolic evaluation for parallelizing compilers, in Proc. of the 11th ACM International Conference on Supercomputing, Vienna, Austria, ACM Press, July 1997, 261–268.Google Scholar
- 4.A.D. Malony and G.V. Wilson, Future directions in parallel performance environment, Performance Measurement and Visualization of Parallel Systems, Eds: G. Haring and G. Kotsis, Elsevier Science Publishers B.V., 1993, 331–351.Google Scholar
- 5.B. P. Miller, M.D. Callaghan, J.M. Cargille, J.K. Hollingsworth, R.B. Irvin, K.L. Karavanic, K. Kunchithapadam, and T. Newhall, The Paradyn parallel performance measurement tools, IEEE Computer 28,11, 1995.Google Scholar
- 6.M. Scheibl, A. Celic, and T. Fahringer, Interfacing Mathematica from the Vienna Fortran Compilation System, Technical Report, Institute for Software Technology and Parallel Systems, Univ. of Vienna, December 1996.Google Scholar
- 8.X.-H. Sun and D. Rover, Scalability of parallel algorithm-machine combinations, IEEE Transactions on Parallel and Distributed Systems, June 1994, 599–613.Google Scholar
- 9.X.-H. Sun, Performance range comparison via crossing point analysis, Lecture Notes in Computer Science 1388 (J. Rolim, ed.), Springer, March 1998.Google Scholar
- 10.X.-H. Sun, T. Fahringer, M. Pantano, and Z. Zhan, SCALA: A performance system for scalable computing, in Proc. of the Workshop on High-Level Parallel Programming Models & Supportive Environments, Lecture Notes in Computer Science 1586, Springer, April 1999.Google Scholar
- 12.X.-H. Sun, D. He, K. Cameron, and Y. Luo, A Factorial Performance Evaluation for Hierarchical Memory Systems, in Proc. of the IEEE Int’l Parallel Processing Symposium’99, April 1999.Google Scholar
- 13.Sun Microsystems Inc., JDBC: A Java SQL API, Version 1.20, http://www.javasoft.com/products/jdbc/index.html, January 1997.
- 14.M. V. Vernon, E. D. Lazowska, and J. Zahorjan, An accurate and efficient performance analysis technique for multi-processor snooping cache-consistency protocols, in Proc. 15 th Annual Symp. Computer Architecture, Honolulu, HI, June 1988, 308–315.Google Scholar