Abstract
It is generally agreed that memory requirements should be taken into account in the scheduling of parallel jobs. However, so far the work on combined processor and memory scheduling has not been based on detailed information and measurements. To rectify this problem, we present an analysis of memory usage by a production workload on a large parallel machine, the 1024-node CM-5 installed at Los Alamos National Lab. Our main observations are
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- The distribution of memory requests has strong discrete components, i.e. some sizes are much more popular than others.
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- Many jobs use a relatively small fraction of the memory available on each node, so there is some room for time slicing among several memory-resident jobs.
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- Larger jobs (using more nodes) tend to use more memory, but it is difficult to characterize the scaling of per-processor memory usage.
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G. Alverson, S. Kahan, R. Korry, C. McCann, and B. Smith, “Scheduling on the Tera MTA”. In Job Scheduling Strategies for Parallel Processing, D. G. Feitelson and L. Rudolph (eds.), pp. 19–44, Springer-Verlag, 1995. Lecture Notes in Computer Science Vol. 949.
G. M. Amdahl, “Validity of the single processor approach to achieving large scale computer capabilities”. In AFIPS Spring Joint Comput. Conf., vol. 30, pp. 483–485, Apr 1967.
D. C. Burger, R. S. Hyder, B. P. Miller, and D. A. Wood, “Paging tradeoffs in distributed-shared-memory multiprocessors”. J. Supercomput. 10(1), pp. 87–104, 1996.
J. J. Dongarra, H. W. Meuer, and E. Strohmaier, “Top500 supercomputer sites”. http://www.netlib.org/benchmark/top500.html. (updated every 6 months).
D. G. Feitelson, “Packing schemes for gang scheduling”. In Job Scheduling Strategies for Parallel Processing, D. G. Feitelson and L. Rudolph (eds.), pp. 89–110, Springer-Verlag, 1996. Lecture Notes in Computer Science Vol. 1162.
D. G. Feitelson, A Survey of Scheduling in Multiprogrammed Parallel Systems. Research Report RC 19790 (87657), IBM T. J. Watson Research Center, Oct 1994.
D. G. Feitelson and M. A. Jette, “Improved utilization and responsiveness with gang scheduling”. In Job Scheduling Strategies for Parallel Processing, D. G. Feitelson and L. Rudolph (eds.), Springer Verlag, 1997. Lecture Notes in Computer Science (this volume).
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. Lecture Notes in Computer Science Vol. 949.
D. G. Feitelson and L. Rudolph, “Parallel job scheduling: issues and approaches”. In Job Scheduling Strategies for Parallel Processing, D. G. Feitelson and L. Rudolph (eds.), pp. 1–18, Springer-Verlag, 1995. Lecture Notes in Computer Science Vol. 949.
D. G. Feitelson and L. Rudolph, “Toward convergence in job schedulers for parallel supercomputers”. In Job Scheduling Strategies for Parallel Processing, D. G. Feitelson and L. Rudolph (eds.), pp. 1–26, Springer-Verlag, 1996. Lecture Notes in Computer Science Vol. 1162.
J. L. Gustafson, “Reevaluating Amdahl's law”. Comm. ACM 31(5), pp. 532–533, May 1988. See also Comm. ACM 32(2), pp. 262–264, Feb 1989, and Comm. ACM 32(8), pp. 1014–1016, Aug 1989.
J. L. Gustafson, G. R. Montry, and R. E. Benner, “Development of parallel methods for a 1024-processor hypercube”. SIAM J. Sci. Statist. Comput. 9(4), pp. 609–638, Jul 1988.
C. McCann and J. Zahorjan, “Scheduling memory constrained jobs on distributed memory parallel computers”. In SIGMETRICS Conf. Measurement éI Modeling of Comput. Syst., pp. 208–219, May 1995.
Minnesota Supercomputer Center, Inc., The Distributed Job Manager Administration Guide. 1993. ftp://ec.msc.edu/pub/LIGHTNING/djm-1.0.O-Src.tar.Z.
E. W. Parsons and K. C. Sevcik, “Coordinated allocation of memory and processors in multiprocessors”. In SIGMETRICS Conf. Measurement & Modeling of Comput. Syst., pp. 57–67, May 1996.
V. G. J. Peris, M. S. Squillante, and V. K. Naik, “Analysis of the impact of memory in distributed parallel processing systems”. In SIGMETRICS Conf. Measurement & Modeling of Comput. Syst., pp. 5–18, May 1994.
S. K. Setia, “The interaction between memory allocation and adaptive partitioning in message-passing multicomputers”. In Job Scheduling Strategies for Parallel Processing, D. G. Feitelson and L. Rudolph (eds.), pp. 146–165, Springer-Verlag, 1995. Lecture Notes in Computer Science Vol. 949.
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.
X-H. Sun and L. M. Ni, “Scalable problems and memory-bounded speedup”. J. Parallel & Distributed Comput. 19(1), pp. 27–37, Sep 1993.
Thinking Machines Corp., Connection Machine CM-5 Technical Summary. Nov 1992.
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.
P. H. Worley, “The effect of time constraints on scaled speedup”. SIAM J. Sci. Statist. Comput. 11(5), pp. 838–858, Sep 1990.
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Feitelson, D.G. (1997). Memory usage in the LANL CM-5 workload. In: Feitelson, D.G., Rudolph, L. (eds) Job Scheduling Strategies for Parallel Processing. JSSPP 1997. Lecture Notes in Computer Science, vol 1291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63574-2_17
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DOI: https://doi.org/10.1007/3-540-63574-2_17
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