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Optimal sharing of bags of tasks in heterogeneous clusters
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Source ACM Symposium on Parallel Algorithms and Architectures archive
Proceedings of the fifteenth annual ACM symposium on Parallel algorithms and architectures table of contents
San Diego, California, USA
SESSION: Scheduling I table of contents
Pages: 1 - 10  
Year of Publication: 2003
ISBN:1-58113-661-7
Authors
Micah Adler  University of Massachusetts, Amherst, MA
Ying Gong  University of Massachusetts, Amherst, MA
Arnold L. Rosenberg  University of Massachusetts, Amherst, MA
Sponsors
ACM: Association for Computing Machinery
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGARCH: ACM Special Interest Group on Computer Architecture
Publisher
ACM  New York, NY, USA
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ABSTRACT

We prove that "FIFO" worksharing protocols provide asymptotically optimal solutions to a problem related to sharing a bag of identically complex tasks in a heterogeneous network of workstations (HNOW) n. In the HNOW-Exploitation Problem, one seeks to accomplish as much work as possible on n during a prespecified fixed period of L time units. The worksharing protocols we study are crafted within an architectural model that characterizes n via parameters that measure workstations' computational and communicational powers. The protocols are self-scheduling, in that they determine completely both an amount of work to allocate to each of n's workstations and a schedule for all related interworkstation communications. A protocol observes a FIFO regimen if it has n's workstations finish their assigned work, and return their results, in the same order in which they are supplied with their workloads. The optimality of FIFO protocols resides in the fact that they accomplish at least as much work as any other protocol during all sufficiently long worksharing episodes. Simulation experiments indicate that the superiority of FIFO protocols is often observed during worksharing episodes of only a few minutes' duration.


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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Collaborative Colleagues:
Micah Adler: colleagues
Ying Gong: colleagues
Arnold L. Rosenberg: colleagues