| Value-maximizing deadline scheduling and its application to animation rendering |
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ACM Symposium on Parallel Algorithms and Architectures
archive
Proceedings of the seventeenth annual ACM symposium on Parallelism in algorithms and architectures
table of contents
Las Vegas, Nevada, USA
SESSION: Miscellaneous
table of contents
Pages: 299 - 308
Year of Publication: 2005
ISBN:1-58113-986-1
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Authors
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Eric Anderson
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Hewlett-Packard Laboratories, Palo Alto, CA
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Dirk Beyer
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Hewlett-Packard Laboratories, Palo Alto, CA
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Kamalika Chaudhuri
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Hewlett-Packard Laboratories, Palo Alto, CA
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Terence Kelly
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Hewlett-Packard Laboratories, Palo Alto, CA
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Norman Salazar
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Hewlett-Packard Laboratories, Palo Alto, CA
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Cipriano Santos
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Hewlett-Packard Laboratories, Palo Alto, CA
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Ram Swaminathan
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Hewlett-Packard Laboratories, Palo Alto, CA
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Robert Tarjan
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Hewlett-Packard Laboratories, Palo Alto, CA
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Janet Wiener
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Hewlett-Packard Laboratories, Palo Alto, CA
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Yunhong Zhou
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Hewlett-Packard Laboratories, Palo Alto, CA
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| Bibliometrics |
Downloads (6 Weeks): 11, Downloads (12 Months): 48, Citation Count: 2
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ABSTRACT
We describe a new class of utility-maximization scheduling problem with precedence constraints, the disconnected staged scheduling problem (DSSP). DSSP is a nonpreemptive multiprocessor deadline scheduling problem that arises in several commercially-important applications, including animation rendering, protein analysis, and seismic signal processing. DSSP differs from most previously-studied deadline scheduling problems because the graph of precedence constraints among tasks within jobs is disconnected, with one component per job. Another difference is that in practice we often lack accurate estimates of task execution times, and so purely offline solutions are not possible. However we do know the set of jobs and their precedence constraints up front and therefore some offline planning is possible.Our solution decomposes DSSP into an offline job selection phase followed by an online task dispatching phase. We model the former as a knapsack problem and explore several solutions to it, describe a new dispatching algorithm for the latter, and compare both with existing methods. Our theoretical results show that while DSSP is NP-hard and inapproximable in general, our two-phase scheduling method guarantees a good performance bound for many special cases. Our empirical results include an evaluation of scheduling algorithms on a real animation-rendering workload; we present a characterization of this workload in a companion paper. The workload records eight weeks of activity on a 1,000-CPU cluster used to render portions of the full-length animated feature film Shrek 2 in 2004. We show that our improved scheduling algorithms can substantially increase the aggregate value of completed jobs compared to existing practices. Our new task dispatching algorithm LCPF performs well by several metrics, including job completion times as well as the aggregate value of completed jobs.
REFERENCES
Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.
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CITED BY 2
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Kimberly Keeton , Terence Kelly , Arif Merchant , Cipriano Santos , Janet Wiener , Xiaoyun Zhu , Dirk Beyer, Don't settle for less than the best: use optimization to make decisions, Proceedings of the 11th USENIX workshop on Hot topics in operating systems, p.1-6, May 07-09, 2007, San Diego, CA
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