Article

Theory and Practice in Parallel Job Scheduling

Authors:

Parkson WongAuthors Info & Claims

IPPS '97: Proceedings of the Job Scheduling Strategies for Parallel Processing

Pages 1 - 34

Published: 05 April 1997 Publication History

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Patki TAhn DMilroy DYeom JGarlick JGrondona MHerbein SScogland T(2023)Fluxion: A Scalable Graph-Based Resource Model for HPC Scheduling ChallengesProceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis10.1145/3624062.3624286(2077-2088)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3624062.3624286
Ghosal DShukla SSim AThakur AWu K(2020)A Reinforcement Learning Based Network Scheduler for Deadline-Driven Data Transfers2019 IEEE Global Communications Conference (GLOBECOM)10.1109/GLOBECOM38437.2019.9013255(1-6)Online publication date: 17-Jun-2020
https://dl.acm.org/doi/10.1109/GLOBECOM38437.2019.9013255
Li BChunduri SHarms KFan YLan ZHoefler TIskra K(2019)The Effect of System Utilization on Application Performance VariabilityProceedings of the 9th International Workshop on Runtime and Operating Systems for Supercomputers10.1145/3322789.3328743(11-18)Online publication date: 17-Jun-2019
https://dl.acm.org/doi/10.1145/3322789.3328743
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Index Terms

Theory and Practice in Parallel Job Scheduling
1. Mathematics of computing
  1. Mathematical software
2. Theory of computation
  1. Design and analysis of algorithms
    1. Approximation algorithms analysis
      1. Scheduling algorithms
    2. Online algorithms
      1. Online learning algorithms
        Scheduling algorithms
  2. Theory and algorithms for application domains
    1. Machine learning theory
      1. Reinforcement learning
        Sequential decision making

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Published In

IPPS '97: Proceedings of the Job Scheduling Strategies for Parallel Processing

April 1997

298 pages

ISBN:3540635742

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 05 April 1997

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Patki TAhn DMilroy DYeom JGarlick JGrondona MHerbein SScogland T(2023)Fluxion: A Scalable Graph-Based Resource Model for HPC Scheduling ChallengesProceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis10.1145/3624062.3624286(2077-2088)Online publication date: 12-Nov-2023
https://dl.acm.org/doi/10.1145/3624062.3624286
Ghosal DShukla SSim AThakur AWu K(2020)A Reinforcement Learning Based Network Scheduler for Deadline-Driven Data Transfers2019 IEEE Global Communications Conference (GLOBECOM)10.1109/GLOBECOM38437.2019.9013255(1-6)Online publication date: 17-Jun-2020
https://dl.acm.org/doi/10.1109/GLOBECOM38437.2019.9013255
Li BChunduri SHarms KFan YLan ZHoefler TIskra K(2019)The Effect of System Utilization on Application Performance VariabilityProceedings of the 9th International Workshop on Runtime and Operating Systems for Supercomputers10.1145/3322789.3328743(11-18)Online publication date: 17-Jun-2019
https://dl.acm.org/doi/10.1145/3322789.3328743
Rebbah MSlimani YDebakla MSmail O(2018)Toward A Performing Resource Provisioning Model for Hybrid CloudInternational Journal of Grid and High Performance Computing10.4018/IJGHPC.201810010210:4(15-42)Online publication date: 1-Oct-2018
https://dl.acm.org/doi/10.4018/IJGHPC.2018100102
Berg BDorsman JHarchol-Balter M(2018)Towards Optimality in Parallel Job SchedulingACM SIGMETRICS Performance Evaluation Review10.1145/3292040.321966646:1(116-118)Online publication date: 12-Jun-2018
https://dl.acm.org/doi/10.1145/3292040.3219666
Berg BDorsman JHarchol-Balter MPsounis KAkella AWierman A(2018)Towards Optimality in Parallel Job SchedulingAbstracts of the 2018 ACM International Conference on Measurement and Modeling of Computer Systems10.1145/3219617.3219666(116-118)Online publication date: 12-Jun-2018
https://dl.acm.org/doi/10.1145/3219617.3219666
Pericàs M(2018)Elastic PlacesACM Transactions on Architecture and Code Optimization10.1145/318545815:2(1-26)Online publication date: 1-May-2018
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Yu LZhou ZFan YPapka MLan Z(2018)System-wide trade-off modeling of performance, power, and resilience on petascale systemsThe Journal of Supercomputing10.1007/s11227-018-2368-874:7(3168-3192)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.1007/s11227-018-2368-8
Wrede FErnsting S(2018)Simultaneous CPU---GPU Execution of Data Parallel Algorithmic SkeletonsInternational Journal of Parallel Programming10.1007/s10766-016-0483-946:1(42-61)Online publication date: 1-Feb-2018
https://dl.acm.org/doi/10.1007/s10766-016-0483-9
Rho JAzumi TNakagawa MSato KNishio N(2017)Scheduling parallel and distributed processing for automotive data stream management systemJournal of Parallel and Distributed Computing10.5555/3143043.3143183109:C(286-300)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.5555/3143043.3143183
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Recommendations

Parallel job scheduling algorithms

Moldable parallel job scheduling using job efficiency: an iterative approach

Towards Optimality in Parallel Job Scheduling

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