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Coscheduling in Clusters: Is It a Viable Alternative?
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Source Conference on High Performance Networking and Computing archive
Proceedings of the 2004 ACM/IEEE conference on Supercomputing table of contents
Page: 16  
Year of Publication: 2004
ISBN:0-7695-2153-3
Authors
Gyu Sang Choi  Penn State University
Jin-Ha Kim  Penn State University
Deniz Ersoz  Penn State University
Andy B. Yoo  Lawrence Livermore National Laboratory
Chita R. Das  Penn State University
Sponsor
SIGARCH: ACM Special Interest Group on Computer Architecture
Publisher
IEEE Computer Society  Washington, DC, USA
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DOI Bookmark: 10.1109/SC.2004.20

ABSTRACT

In this paper, we conduct an in-depth evaluation of a broad spectrum of scheduling alternatives for clusters. These include the widely used batch scheduling, local scheduling, gang scheduling, all prior communication-driven coscheduling algorithms (Dynamic Coscheduling (DCS), Spin Block (SB), Periodic Boost (PB), and Co-ordinated Coscheduling (CC)) and a newly proposed HYBRID coscheduling algorithm on a 16-node, Myrinet-connected Linux cluster. Performance and energy measurements using several NAS, LLNL and ANL benchmarks on the Linux cluster provide several interesting conclusions. First, although batch scheduling is currently used in most clusters, all blocking-based coscheduling techniques such as SB, CC and HYBRID and the gang scheduling can provide much better performance even in a dedicated cluster platform. Second, in contrast to some of the prior studies, we observe that blocking-based schemes like SB and HYBRID can provide better performance than spin-based techniques like PB on a Linux platform. Third, the proposed HYBRID scheduling provides the best performance-energy behavior and can be implemented on any cluster with little effort. All these results suggest that blocking-based coscheduling techniques are viable candidates to be used in clusters for significant performance-energy benefits.


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:
Gyu Sang Choi: colleagues
Jin-Ha Kim: colleagues
Deniz Ersoz: colleagues
Andy B. Yoo: colleagues
Chita R. Das: colleagues