Browse > Article

A Methodolgy to Evaluate Program Tuning Alternatives  

Eom, Hyeon-Sang (서울대학교 컴퓨터공학부)
Abstract
We introduce a new performance evaluation methodology that helps programmers evaluate different tuning alternatives in order to improve program performance. This methodology permits measuring performance implications of using tuning alternatives. Specifically, the methodology predicts performance after workload migration for a distributed or parallel program in contrast to traditional performance methodlogies that quantify time spent in program components for bottleneck identification. The methodology thus provides guidance on workload migration. The methodology also permits predicting the performance impact of changing the underlying network. The methodology may evaluate performance incrementally and online during the execution of the program to be tuned. We show that our methodology, when it is implemented and used, permits accurately predicting the performance of different tuning alternatives for a test suite of six programs.
Keywords
Evaluation of Performance Tuning Alternatives; Measurements; Prediction; Tools; Online Analysis; Distributed and Parallel Computing;
Citations & Related Records
연도 인용수 순위
  • Reference
1 W. Gu, G. Eisenhauer, E. Kraemer, K. Schwan, J, Stasko, J, Vetter, and N. Mallavurupu, 'Falcon: On-line Monitoring and Steering of Large-Scale Parallel Programs,' Frontiers '95, McLean, VA, pp. 422-429, Feb. 6-9, 1995   DOI
2 W. Williams, T. Hoel, and D. Pase, The MPP Apprentice Performance Tool: Delivering the Performance of the Cray T3D, Programming Environments for Massively Parallel Distributed Systems, North-Holland, 1994
3 D. A. Reed, R. A. Aydt, R. J. Noe, P. C. Roth, K. A. Shields, B. W. Schwartz, and L. F. Tavera, Scalable Performance Analysis: The Pablo Performance, Analysis Environment, Scalable Parallel Libraries Conference, A. Skjellum, Editor, IEEE Computer Society, pp. 104-113, 1993   DOI
4 J. K. Hollingsworth and B. P, Miller, 'Dynamic Control of Performance Monitoring on Large Scale Parallel Systems,' 7th ACM International Conference on Supercomputing (ICS), Tokyo, Japan, pp. 185-194, July 1993   DOI
5 F. Lange, R. Kroger, and M. Gergeleit, 'JEWEL: Design and Implementation of a Distributed Measurement System,' IEEE Transactions on Parallel and Distributed Systems, Vo1.3, No.6, pp. 657-671, 1992   DOI   ScienceOn
6 J. K. Hollingsworth, 'An Online Computation of Critical Path Profiling,' SPDT'96: SIGMETRICS Symposium on Parallel and Distributed Tools, Philadelphia, PA, pp. H-20, May 22-23,1996   DOI
7 M. Martonosi, A. Gupta, and T. Anderson, 'MemSpy: Analyzing Memory System Bottlenecks in Programs,' SIGMETRICS, Newport, RI, pp. 1-12, June 1-5, 1992
8 A. J. Goldberg and J. L. Hennessy, 'Performance Debugging Shared Memory Multiprocessor Programs with MTOOL,' Supercomputing '91, Albuquerque, NM, pp. 481-490, Nov. 18-22, 1991   DOI
9 B. P. Miller, M. D. Callaghan, J. M. Cargille, J. K. Hollingsworth, R. B. Irvin, K. 1. Karavanic, K. Kunchithapadarn, and T. Newhall, 'The Paradyn Parallel Performance Measurement Tools,' IEEE Computer, Vo1.28, No.H, pp. 37-46, 1995   DOI   ScienceOn
10 A. Geist, A. Beguelin, J. Dongarra, W. Jiang, R. Manchek, and V. Sunderam, PVM: Parallel Virtual Machine, The MIT Press, Cambridge, Mass, 1994
11 M. E. Crovella and T. J, LeBlanc, 'Parallel Performance Prediction Using Lost Cycles,' Supercomputing '94, Washington DC, pp. 600-609, Nov. 14-18, 1994   DOI
12 D. Kimelman and D. Zernik, 'On-the-Fly Topological Sort - A Basis for Interactive Debugging and Live Visuali-ation of Parallel Programs,' ACM/ONR Workshop on Parallel and Distributed Debugging, San Diego, CA, pp. 12-20, May 17-18, 1993   DOI
13 L. Lamport, 'Time, Clocks, and the Ordering of Events in a Distributed System,' Comm. ACM, Vo1.21, No.7, pp. 558-564, 1978   DOI   ScienceOn
14 S. K. Reinhart, J. R. Lams, and D. A. Wood, 'The Wisconsin Wind Tunnel: Virtual Prototyping of Parallel Computers,' SIGMETIRCS, pp. 46-60, May 1993   DOI
15 V. Balasundaram, G. Fox, K. Kennedy, and U. Kremer, 'A Static Performance Estimator to Guide Data Partitioning Decisions,' 1991 ACM SIGPLAN Symposium on Principals and Practice of Parallel Programming, Williamsburg, VA, pp. 213-223, April 21-24, 1991   DOI
16 A. J, C. v. Gemund, 'Performance Prediction of Parallel Processing Systems: The PAMELA Methodology,' 7th ACM International Conference on Supercomputing (ICS), Tokyo, Japan, pp. 318-327, July 1993   DOI
17 D. A. Reed, K. A. Shields, W. H. Scullin, L. F. Tavera, and C. L. Ellford, 'Virtual Reality and Parallel Systems Performance Analysis,' IEEE Computer, Vo1.28, No.11, pp. 57-68, 1995   DOI   ScienceOn