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Job Scheduling Strategies for Parallel Processing [electronic resource] : 26th Workshop, JSSPP 2023, St. Petersburg, FL, USA, May 19, 2023, Revised Selected Papers / edited by Dalibor Klusáček, Julita Corbalán, Gonzalo P. Rodrigo.

Contributor(s): Material type: TextTextLanguage: English Series: Publication details: Cham : Springer Nature Switzerland : Imprint: Springer, 2023.Edition: 1st ed. 2023Description: IX, 193 p. 66 illus., 57 illus. in color. online resourceISBN:
  • 9783031439438
Subject(s): DDC classification:
  • 005.1 23
Online resources:
Contents:
Architecture of the Slurm Workload Manager -- Asynchronous Execution of Heterogeneous Tasks in ML-driven HPC Workflows -- Memory-Aware Latency Prediction Model for Concurrent Kernels in Partitionable GPUs: Simulations and Experiments -- Stragglers in Distributed Matrix Multiplication -- Optimization Metrics for the Evaluation of Batch Schedulers in HPC -- An experimental analysis of regression-obtained HPC scheduling heuristics -- An efficient approach based on graph neural networks for predicting wait time in job schedulers -- Evaluating the Potential of Coscheduling on High-Performance Computing Systems -- Scaling Optimal Allocation of Cloud Resources Using Lagrange Relaxation.
Summary: This book constitutes the thoroughly refereed post-conference proceedings of the 26th International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP 2023, held in St. Petersburg, FL, USA, during May 19, 2023. The 8 full papers and one keynote paper included in this book were carefully reviewed and selected from 14 submissions. The volume contains two sections: keynote and technical papers.
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Item type Current library Call number Materials specified Status Date due Barcode Item holds
E-Books E-Books National Library of India Online Resource 005.1 (Browse shelf(Opens below)) Available EBK000045836ENG
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Architecture of the Slurm Workload Manager -- Asynchronous Execution of Heterogeneous Tasks in ML-driven HPC Workflows -- Memory-Aware Latency Prediction Model for Concurrent Kernels in Partitionable GPUs: Simulations and Experiments -- Stragglers in Distributed Matrix Multiplication -- Optimization Metrics for the Evaluation of Batch Schedulers in HPC -- An experimental analysis of regression-obtained HPC scheduling heuristics -- An efficient approach based on graph neural networks for predicting wait time in job schedulers -- Evaluating the Potential of Coscheduling on High-Performance Computing Systems -- Scaling Optimal Allocation of Cloud Resources Using Lagrange Relaxation.

This book constitutes the thoroughly refereed post-conference proceedings of the 26th International Workshop on Job Scheduling Strategies for Parallel Processing, JSSPP 2023, held in St. Petersburg, FL, USA, during May 19, 2023. The 8 full papers and one keynote paper included in this book were carefully reviewed and selected from 14 submissions. The volume contains two sections: keynote and technical papers.

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