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Job Scheduling Strategies for Parallel Processing

19th and 20th International Workshops, JSSPP 2015, Hyderabad, India, May 26, 2015 and JSSPP 2016, Chicago, IL, USA, May 27, 2016, Revised Selected Papers

  • Conference proceedings
  • © 2017

Overview

Editors:
  1. Narayan Desai

    1. Google, Seattle, USA

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    You can also search for this editor in PubMed   Google Scholar

  2. Walfredo Cirne

    1. Google, Mountain View, USA

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

Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)

Included in the following conference series:

Conference proceedings info: JSSPP 2015. JSSPP 2016.

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Table of contents (15 papers)

  1. Front Matter

    Pages I-X
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  2. JSSPP 2015

    1. Front Matter

      Pages 1-1
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    2. Controlled Duplication Scheduling of Real-Time Precedence Tasks on Heterogeneous Multiprocessors

      • Jagpreet Singh, Nitin Auluck
      Pages 3-21

    3. On the Design and Implementation of an Efficient Lock-Free Scheduler

      • Florian Negele, Felix Friedrich, Suwon Oh, Bernhard Egger
      Pages 22-45

    4. Scheduling for Better Energy Efficiency on Many-Core Chips

      • Chanseok Kang, Seungyul Lee, Yong-Jun Lee, Jaejin Lee, Bernhard Egger
      Pages 46-68

    5. Data Driven Scheduling Approach for the Multi-node Multi-GPU Cholesky Decomposition

      • Yuki Tsujita, Toshio Endo
      Pages 69-82

    6. Real-Life Experience with Major Reconfiguration of Job Scheduling System

      • Dalibor Klusáček, Šimon Tóth, Gabriela Podolníková
      Pages 83-101

    7. Evalix: Classification and Prediction of Job Resource Consumption on HPC Platforms

      • Joseph Emeras, Sébastien Varrette, Mateusz Guzek, Pascal Bouvry
      Pages 102-122

    8. Influence of Dynamic Think Times on Parallel Job Scheduler Performances in Generative Simulations

      • Stephan Schlagkamp
      Pages 123-137

  3. JSSPP 2016

    1. Front Matter

      Pages 139-139
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    2. Automatic Co-scheduling Based on Main Memory Bandwidth Usage

      • Jens Breitbart, Josef Weidendorfer, Carsten Trinitis
      Pages 141-157

    3. Adaptive Space-Shared Scheduling for Shared-Memory Parallel Programs

      • Younghyun Cho, Surim Oh, Bernhard Egger
      Pages 158-177

    4. Batsim: A Realistic Language-Independent Resources and Jobs Management Systems Simulator

      • Pierre-François Dutot, Michael Mercier, Millian Poquet, Olivier Richard
      Pages 178-197

    5. Planning and Metaheuristic Optimization in Production Job Scheduler

      • Dalibor Klusáček, Václav Chlumský
      Pages 198-216

    6. Topology-Aware Scheduling on Blue Waters with Proactive Queue Scanning and Migration-Based Job Placement

      • Kangkang Li, Maciej Malawski, Jarek Nabrzyski
      Pages 217-231

    7. Analyzing the Performance of Allocation Strategies Based on Space-Filling Curves

      • Jose A. Pascual, Jose A. Lozano, Jose Miguel-Alonso
      Pages 232-251

    8. Choosing Optimal Maintenance Time for Stateless Data-Processing Clusters

      • Zhenyun Zhuang, Min Shen, Haricharan Ramachandra, Suja Viswesan
      Pages 252-273

    9. Open Issues in Cloud Resource Management

      • Narayan Desai, Walfredo Cirne
      Pages 274-278

  4. Back Matter

    Pages 279-279
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  1. Job Scheduling Strategies for Parallel Processing

Keywords

About this book

This book constitutes the thoroughly refereed post-conference proceedings of the 19th and 20th International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP 2015 and 2016, held respectively in Hyderabad, India, on May 26, 2015 and in Chicago, IL, USA, on May 27, 2016. The 14 revised full papers presented (7 papers in 2015 and 7 papers in 2016) were carefully reviewed and selected from 28 submissions (14 in 2015 and 14 in 2016).

The papers cover the following topics: parallel scheduling raising challenges multiple levels of abstractions; node level parallelism; minimization of energy consumption in task migration within a many-core chip; task  replication in real-time scheduling context; data-driven approach to schedule GPU load; the use of lock-free data structures in OS scheduler; the influence  between user behaviour (think time, more precisely) and parallel scheduling; Evalix, a predictor for job resource consumption; sophisticated and realistic simulation; space-filling curves leading to better scheduling of large-scale computers; discussion of real-life production experiences.

Editors and Affiliations

  • Google, Seattle, USA

    Narayan Desai

  • Google, Mountain View, USA

    Walfredo Cirne

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