NDLI: Benchmarking and Analysis of Variations of Work Stealing Scheduler on Clustered System

Content Provider	IEEE Xplore Digital Library
Author	Kumar, S. Sahu, A.
Copyright Year	2014
Description	Author affiliation: Deptt. of Comp. Sc. & Eng., Indian Inst. of Technol. Guwahati, Guwahati, India (Kumar, S.; Sahu, A.)
Abstract	Classical work stealing is an efficient dynamic load-balancing technique in shared memory multiprocessor or multicore system. But the performance of the same classical work scheduling on cluster chip multicore is not appreciable. So modification to this is necessary to improve performance. In this paper, we have discussed many earlier proposed modifications, and also proposed some simplistic modifications to suite targeted clustered environment. We have described a methodology to evaluate all the variations of work stealing analytically and experimentally on multiprocessor simulator and on real platform. Our methodology of evaluation include designing of novel parametric synthetic benchmark, which can be used to mimic behavior (or profile) of many real life benchmarks. The designed synthetic benchmark caters a wide range of application profiles to evaluate the design space of both variations of work stealing algorithms and clustered chip multiprocessor. In this work, we found that if the number of available parallelism of the targeted application is higher and data sharing between tasks is high then one of the proposed modification of work stealing (probabilistic based victim search and threshold on size of migratable task) outperform the rest of the modifications.
Starting Page	28
Ending Page	35
File Size	425924
Page Count	8
File Format	PDF
ISBN	9781479983346
DOI	10.1109/PDCAT.2014.13
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multicore processing Benchmark testing Clustering algorithms Probabilistic logic Sockets Receivers Load management benchmarking and analysis work stealing scheduler cluster
Content Type	Text
Resource Type	Article

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