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Dynamic load balancing of SAMR applications on distributed systems
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Source Conference on High Performance Networking and Computing archive
Proceedings of the 2001 ACM/IEEE conference on Supercomputing (CDROM) table of contents
Denver, Colorado
Pages: 36 - 36  
Year of Publication: 2001
ISBN:1-58113-293-X
Authors
Zhiling Lan  Northwestern University, Evanston, IL
Valerie E. Taylor  Northwestern University, Evanston, IL
Greg Bryan  Massachusetts Institute of Technology, Cambridge, MA
Sponsors
ACM: Association for Computing Machinery
SIGARCH: ACM Special Interest Group on Computer Architecture
IEEE-CS\DATC : IEEE Computer Society
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 13,   Downloads (12 Months): 56,   Citation Count: 12
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ABSTRACT

Dynamic load balancing(DLB) for parallel systems has been studied extensively; however, DLB for distributed systems is relatively new. To efficiently utilize computing resources provided by distributed systems, an underlying DLB scheme must address both heterogeneous and dynamic features of distributed systems. In this paper, we propose a DLB scheme for Structured Adaptive Mesh Refinement(SAMR) applications on distributed systems. While the proposed scheme can take into consideration (1) the heterogeneity of processors and (2) the heterogeneity and dynamic load of the networks, the focus of this paper is on the latter. The load-balancing processes are divided into two phases: global load balancing and local load balancing. We also provide a heuristic method to evaluate the computational gain and redistribution cost for global redistribution. Experiments show that by using our distributed DLB scheme, the execution time can be reduced by 9%-46% as compared to using parallel DLB scheme which does not consider the heterogeneous and dynamic features of distributed systems.


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  12

Collaborative Colleagues:
Zhiling Lan: colleagues
Valerie E. Taylor: colleagues
Greg Bryan: colleagues