Job Scheduling Strategies for Parallel Processing: 26th Workshop, JSSPP 2023, St. Petersburg, FL, USA,...

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Dalibor Klusáček Julita Corbalán

Job Scheduling Strategies for Parallel Processing

26th Workshop, JSSPP 2023, St. Petersburg, FL, USA, May 19, 2023, Revised Selected Papers

Dalibor Klusáček, Julita Corbalán, Gonzalo P. Rodrigo

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English

Springer International Publishing AG
15 September 2023

Information theory; Coding theory & cryptology; Software Engineering; Parallel processing; Artificial intelligence

Series: Lecture Notes in Computer Science

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.

Edited by: Dalibor Klusáček, Julita Corbalán, Gonzalo P. Rodrigo
Imprint: Springer International Publishing AG
Country of Publication: Switzerland
Edition: 1st ed. 2023
Volume: 14283
Dimensions: Height: 235mm, Width: 155mm,
Weight: 326g
ISBN: 9783031439421
ISBN 10: 3031439422
Series: Lecture Notes in Computer Science
Pages: 193
Publication Date: 15 September 2023
Audience: Professional and scholarly , Undergraduate
Format: Paperback
Publisher's Status: Active

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.