NDLI: Empirical evaluation of shared parallel execution on independently scheduled clusters

Content Provider	IEEE Xplore Digital Library
Author	Ghanesh, M. Kumar, S. Subhlok, J.
Copyright Year	2005
Description	Author affiliation: Dept. of Comput. Sci., Houston Univ., TX, USA (Ghanesh, M.; Kumar, S.; Subhlok, J.)
Abstract	Parallel machines are typically space shared, or time shared such that only one application executes on a group of nodes at any given time. It is generally assumed that executing multiple parallel applications simultaneously on a group of independently scheduled nodes is not efficient because of synchronization requirements. The central contribution of this paper is to demonstrate that performance of parallel applications with sharing is typically competitive for independent and coordinated (gang) scheduling on small compute clusters. There is a modest overhead due to uncoordinated scheduling but it is often compensated by better sharing of resources. The impact of sharing was studied for different numbers of nodes and threads and different memory and CPU requirements of competing applications. The significance of the CPU time slice, a key parameter in CPU scheduling, was also studied. Application characteristics and operating system scheduling policies are identified as the main factors that influence performance with node sharing. All experiments are performed with NAS benchmarks on a Linux cluster. The significance of this research is that it provides evidence to support flexible and decentralized scheduling and resource selection policies for cluster and grid environments.
Starting Page	309
Ending Page	316
File Size	4683856
Page Count	8
File Format	PDF
ISBN	0780390741
DOI	10.1109/CCGRID.2005.1558570
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-05-09
Publisher Place	United Kingdom
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Processor scheduling Yarn Operating systems Communication system control Application software Grid computing High performance computing Switches Computer science Parallel machines
Content Type	Text
Resource Type	Article

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