Abstract
The performance of a computer system depends not only on its design and implementation, but also on the workloads it has to handle. Indeed, in some cases the workload can sway performance evaluation results. It is therefore crucially important that representative workloads be used for performance evaluation. This can be done by analyzing and modeling existing workloads. However, as more sophisticated workload models become necessary, there is an increasing need for the collection of more detailed data about workloads. This has to be done with an eye for those features that are really important.
Chapter PDF
References
S-H. Chiang and M. K. Vernon, “Characteristics of a large shared memory production workload”. In Job Scheduling Strategies for Parallel Processing, D. G. Feitelson and L. Rudolph (eds.), pp. 159–187, Springer Verlag, 2001. Lect. Notes Comput. Sci. vol. 2221.
W. Cirne and F. Berman, “A comprehensive model of the supercomputer workload”. In 4th Workshop on Workload Characterization, Dec 2001.
W. Cirne and F. Berman, “A model for moldable supercomputer jobs”. In 15th Intl. Parallel & Distributed Processing Symp., Apr 2001.
M. E. Crovella, “Performance evaluation with heavy tailed distributions”. In Job Scheduling Strategies for Parallel Processing, D. G. Feitelson and L. Rudolph (eds.), pp. 1–10, Springer Verlag, 2001. Lect. Notes Comput. Sci. vol. 2221.
R. Cypher, A. Ho, S. Konstantinidou, and P. Messina, “A quantitative study of parallel scientific applications with explicit communication”. J. Supercomput. 10(1), pp. 5–24, 1996.
A. B. Downey, “A parallel workload model and its implications for processor allocation”. In 6th Intl. Symp. High Performance Distributed Comput., Aug 1997.
A. B. Downey and D. G. Feitelson, “The elusive goal of workload characterization”. Performance Evaluation Rev. 26(4), pp. 14–29, Mar 1999.
D. G. Feitelson, Analyzing the Root Causes of Performance Evaluation Results. Technical Report 2002-4, School of Computer Science and Engineering, Hebrew University, Mar 2002.
D. G. Feitelson, “The effect of workloads on performance evaluation”. In Performance Evaluation of Complex Systems: Techniques and Tools, M. Calzarossa (ed.), Springer-Verlag, Sep 2002. Lect. Notes Comput. Sci. Tutorials.
D. G. Feitelson, “Memory usage in the LANL CM-5 workload”. In Job Scheduling Strategies for Parallel Processing, D. G. Feitelson and L. Rudolph (eds.), pp. 78–94, Springer Verlag, 1997. Lect. Notes Comput. Sci. vol. 1291.
D. G. Feitelson and B. Nitzberg, “Job characteristics of a production parallel scientific workload on the NASA Ames iPSC/860”. In Job Scheduling Strategies for Parallel Processing, D. G. Feitelson and L. Rudolph (eds.), pp. 337–360, Springer-Verlag, 1995. Lect. Notes Comput. Sci. vol. 949.
D. G. Feitelson and L. Rudolph, “Evaluation of design choices for gang scheduling using distributed hierarchical control”. J. Parallel & Distributed Comput. 35(1), pp. 18–34, May 1996.
D. G. Feitelson and L. Rudolph, “Metrics and benchmarking for parallel job scheduling”. In Job Scheduling Strategies for Parallel Processing, D. G. Feitelson and L. Rudolph (eds.), pp. 1–24, Springer-Verlag, 1998. Lect. Notes Comput. Sci. vol. 1459.
S. Hotovy, “Workload evolution on the Cornell Theory Center IBM SP2”. In Job Scheduling Strategies for Parallel Processing, D. G. Feitelson and L. Rudolph (eds.), pp. 27–40, Springer-Verlag, 1996. Lect. Notes Comput. Sci. vol. 1162.
J. Jann, P. Pattnaik, H. Franke, F. Wang, J. Skovira, and J. Riodan, “Modeling of workload in MPPs”. In Job Scheduling Strategies for Parallel Processing, D. G. Feitelson and L. Rudolph (eds.), pp. 95–116, Springer Verlag, 1997. Lect. Notes Comput. Sci. vol. 1291.
P. Krueger, T-H. Lai, and V. A. Dixit-Radiya, “Job scheduling is more important than processor allocation for hypercube computers”. IEEE Trans. Parallel & Distributed Syst. 5(5), pp. 488–497, May 1994.
V. Lo, J. Mache, and K. Windisch, “A comparative study of real workload traces and synthetic workload models for parallel job scheduling”. In Job Scheduling Strategies for Parallel Processing, D. G. Feitelson and L. Rudolph (eds.), pp. 25–46, Springer Verlag, 1998. Lect. Notes Comput. Sci. vol. 1459.
U. Lublin and D. G. Feitelson, The Workload on Parallel Supercomputers: Modeling the Characteristics of Rigid Jobs. Technical Report 2001-12, Hebrew University, Oct 2001.
A. W. Mu’alem and D. G. Feitelson, “Utilization, predictability, workloads, and user runtime estimates in scheduling the IBM SP2 with backfilling”. IEEE Trans. Parallel & Distributed Syst. 12(6), pp. 529–543, Jun 2001.
T. D. Nguyen, R. Vaswani, and J. Zahorjan, “Parallel application characterization for multiprocessor scheduling policy design”. In Job Scheduling Strategies for Parallel Processing, D. G. Feitelson and L. Rudolph (eds.), pp. 175–199, Springer-Verlag, 1996. Lect. Notes Comput. Sci. vol. 1162.
N. Nieuwejaar, D. Kotz, A. Purakayastha, C. S. Ellis, and M. L. Best, “File-access characteristics of parallel scientific workloads”. IEEE Trans. Parallel & Distributed Syst. 7(10), pp. 1075–1089, Oct 1996.
Parallel workloads archive. URL http://www.cs.huji.ac.il/labs/parallel/workload/.
J. P. Singh, J. L. Hennessy, and A. Gupta, “Scaling parallel programs for multiprocessors: methodology and examples”. Computer 26(7), pp. 42–50, Jul 1993.
E. Smirni and D. A. Reed, “Workload characterization of input/output intensive parallel applications”. In 9th Intl. Conf. Comput. Performance Evaluation, pp. 169–180, Springer-Verlag, Jun 1997. Lect. Notes Comput. Sci. vol. 1245.
J. S. Vetter and F. Mueller, “Communication characteristics of large-scale scientific applications for contemporary cluster architectures”. In 16th Intl. Parallel & Distributed Processing Symp., May 2002.
K. Y. Wang and D. C. Marinescu, “Correlation of the paging activity of individual node programs in the SPMD execution model”. In 28th Hawaii Intl. Conf. System Sciences, vol. I, pp. 61–71, Jan 1995.
K. Windisch, V. Lo, R. Moore, D. Feitelson, and B. Nitzberg, “A comparison of workload traces from two production parallel machines”. In 6th Symp. Frontiers Massively Parallel Comput., pp. 319–326, Oct 1996.
A. Wong, L. Oliker, W. Kramer, T. Kaltz, and D. Bailey, “System utilization benchmark on the Cray T3E and IBM SP2”. In Job Scheduling Strategies for Parallel Processing, D. G. Feitelson and L. Rudolph (eds.), pp. 56–67, Springer Verlag, 2000. Lect. Notes Comput. Sci. vol. 1911.
S. C. Woo, M. Ohara, E. Torrie, J. P. Singh, and A. Gupta, “The SPLASH-2 programs: characterization and methodological considerations”. In 22nd Ann. Intl. Symp. Computer Architecture Conf. Proc., pp. 24–36, Jun 1995.
P. H. Worley, “The effect of time constraints on scaled speedup”. SIAM J. Sci. Statist. Comput. 11(5), pp. 838–858, Sep 1990.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Feitelson, D.G. (2002). The Forgotten Factor: Facts on Performance Evaluation and Its Dependence on Workloads. In: Monien, B., Feldmann, R. (eds) Euro-Par 2002 Parallel Processing. Euro-Par 2002. Lecture Notes in Computer Science, vol 2400. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45706-2_4
Download citation
DOI: https://doi.org/10.1007/3-540-45706-2_4
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44049-9
Online ISBN: 978-3-540-45706-0
eBook Packages: Springer Book Archive