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Adaptive Space-Shared Scheduling for Shared-Memory Parallel Programs

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Job Scheduling Strategies for Parallel Processing (JSSPP 2015, JSSPP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10353))

Abstract

Space-sharing is regarded as the proper resource management scheme for many-core OSes. For today’s many-core chips and parallel programming models providing no explicit resource requirements, an important research problem is to provide a proper resource allocation to the running applications while considering not only the architectural features but also the characteristics of the parallel applications.

In this paper, we introduce a space-shared scheduling strategy for shared-memory parallel programs. To properly assign the disjoint set of cores to simultaneously running parallel applications, the proposed scheme considers the performance characteristics of the executing (parallel) code section of all running applications. The information about the performance is used to compute a proper core allocation in accordance to the goal of the scheduling policy given by the system manager.

We have first implemented a user-level scheduling framework that runs on Linux-based multi-core chips. A simple performance model based solely on online profile data is used to characterize the performance scalability of applications. The framework is evaluated for two scheduling policies, balancing and maximizing QoS, and on two different many-core platforms, a 64-core AMD Opteron platform and a 36-core Tile-Gx36 processor. Experimental results of various OpenMP benchmarks show that in general our space-shared scheduling outperforms the standard Linux scheduler and meets the goal of the active scheduling policy.

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Acknowledgments

This work was supported, in part, by BK21 Plus for Pioneers in Innovative Computing (Dept. of Computer Science and Engineering, SNU) funded by the National Research Foundation (NRF) of Korea (Grant 21A20151113068), the Basic Science Research Program through NRF funded by the Ministry of Science, ICT & Future Planning (Grant NRF-2015K1A3A1A14021288), and by the Promising-Pioneering Researcher Program through Seoul National University in 2015. ICT at Seoul National University provided research facilities for this study.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Seoul National University, Seoul, Korea

    Younghyun Cho, Surim Oh & Bernhard Egger

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  1. Younghyun Cho
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  2. Surim Oh
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  3. Bernhard Egger
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Correspondence to Bernhard Egger .

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Editors and Affiliations

  1. Google, Seattle, USA

    Narayan Desai

  2. Google, Mountain View, USA

    Walfredo Cirne

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Cho, Y., Oh, S., Egger, B. (2017). Adaptive Space-Shared Scheduling for Shared-Memory Parallel Programs. In: Desai, N., Cirne, W. (eds) Job Scheduling Strategies for Parallel Processing. JSSPP JSSPP 2015 2016. Lecture Notes in Computer Science(), vol 10353. Springer, Cham. https://doi.org/10.1007/978-3-319-61756-5_9

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  • DOI: https://doi.org/10.1007/978-3-319-61756-5_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61755-8

  • Online ISBN: 978-3-319-61756-5

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