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Job Scheduling Strategies for Parallel Processing

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Cover of 'Job Scheduling Strategies for Parallel Processing'

Table of Contents

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    Book Overview
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    Chapter 1 Web-Scale Job Scheduling
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    Chapter 2 DEMB: Cache-Aware Scheduling for Distributed Query Processing
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    Chapter 3 Employing Checkpoint to Improve Job Scheduling in Large-Scale Systems
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    Chapter 4 Multi-objective Processor-Set Selection for Computational Cluster-Systems
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    Chapter 5 On Workflow Scheduling for End-to-End Performance Optimization in Distributed Network Environments
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    Chapter 6 Dynamic Kernel/Device Mapping Strategies for GPU-Assisted HPC Systems
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    Chapter 7 Optimal Co-Scheduling to Minimize Makespan on Chip Multiprocessors
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    Chapter 8 Evaluating Scalability and Efficiency of the Resource and Job Management System on Large HPC Clusters
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    Chapter 9 Partitioned Parallel Job Scheduling for Extreme Scale Computing
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    Chapter 10 High-Resolution Analysis of Parallel Job Workloads
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    Chapter 11 Identifying Quick Starters: Towards an Integrated Framework for Efficient Predictions of Queue Waiting Times of Batch Parallel Jobs
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    Chapter 12 On Identifying User Session Boundaries in Parallel Workload Logs
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    Chapter 13 Performance and Fairness for Users in Parallel Job Scheduling
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    Chapter 14 Comprehensive Workload Analysis and Modeling of a Petascale Supercomputer
Attention for Chapter 10: High-Resolution Analysis of Parallel Job Workloads
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Citations

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Chapter title
High-Resolution Analysis of Parallel Job Workloads
Chapter number 10
Book title
Job Scheduling Strategies for Parallel Processing
Published by
Springer, Berlin, Heidelberg, May 2012
DOI 10.1007/978-3-642-35867-8_10
Book ISBNs
978-3-64-235866-1, 978-3-64-235867-8
Authors

David Krakov, Dror G. Feitelson

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 60%
Researcher 1 20%
Other 1 20%
Readers by discipline Count As %
Computer Science 4 80%
Business, Management and Accounting 1 20%

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