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    <title>Euro-Par 2023: Parallel Processing Workshops</title>
    <subTitle>Euro-Par 2023 International Workshops, Limassol, Cyprus, August 28 – September 1, 2023, Revised Selected Papers, Part II</subTitle>
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    <namePart>Di Sanzo, Pierangelo.</namePart>
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    <dateIssued encoding="marc">2024</dateIssued>
    <edition>1st ed. 2024.</edition>
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    <extent>XVII, 337 p. 95 illus., 76 illus. in color. online resource.</extent>
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  <abstract>Chapters “1, 2, 3, 4, 5, 6, 7, 13, 20 and 31” is available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.</abstract>
  <tableOfContents>The 2nd International Workshop on Resource AWareness of Systems and Society (RAW 2023) -- Performance and energy aware training of a deep neural network in a multi-GPU environment with power capping -- GPPRMon: GPU Runtime Memory Performance and Power Monitoring Tool -- Towards Resource-Efficient DNN Deployment for Traffic Object Recognition: From Edge to Fog -- The Implementation of Battery Charging Strategy for IoT Nodes -- subMFL: Compatible subModel Generation for Federated Learning in Device Heterogeneous Environment -- Towards a Simulation as a Service Platform for the Cloud-to-Things Continuum -- Cormas: The Software for Participatory Modelling and its Application for Managing Natural Resources in Senegal -- Asynchronous Many-Task systems for Exascale (AMTE) -- Malleable APGAS Programs and their Support in Batch Job Schedulers -- Task-Level Checkpointing for Nested Fork-Join Programs using Work Stealing -- Making Uintah Performance Portable for Department of Energy Exascale Testbeds -- Benchmarking the Parallel 1D Heat Equation Solver in Chapel, Charm++, C++, HPX, Go, Julia, Python, Rust, Swift, and Java -- PECS 2023 - 2-page report -- Parallel auto-scheduling of counting queries in machine learning applications on HPC systems -- Energy Efficiency Impact of Processing in Memory: A Comprehensive Review of Workloads on the UPMEM Architecture -- Enhancing Supercomputer Performance with Malleable Job Scheduling Strategies -- A Performance Modelling-driven Approach to Hardware Resource Scaling -- Applications and Benefits of UPMEM commercial Massively parallel Processing-In-Memory (PIM) Platform (ABUMPIMP) Minisymposium -- Adaptive HPC Input/Output Systems -- Dynamic Allocations in a Hierarchical Parallel Context -- Designing A Sustainable Serverless Graph Processing Tool on the Computing Continuum -- Diorthotis: A Parallel Batch Evaluator for Programming Assignments -- Experiences and Lessons Learned from PHYSICS: A Framework for Cloud Development with FaaS -- Improved IoT Application Placement in Fog Computing through Postponement -- High-Performance Distributed Computing with Smartphones -- Blockchain-based Decentralized Authority for Complex Organizational Structures Management -- Transparent Remote OpenMP Offloading based on MPI -- DAPHNE Runtime: Harnessing Parallelism for Integrated Data Analysis Pipelines -- Exploring Factors Impacting Data Offloading Performance in Edge and Cloud Environments -- HEAppE Middleware: From desktop to HPC -- Towards Energy-Aware Machine Learning in Geo-Distributed IoT Settings -- OpenCUBE: Building an Open Source Cloud Blueprint with EPI Systems -- BDDC Preconditioning in the Microcard Project -- Online Job Failure Prediction in an HPC system -- Exploring Mapping Strategies for Co-allocated HPC Applications -- A polynomial-time algorithm for detecting potentially unbounded places in a Petri net-based concurrent system -- Data Assimilation with Ocean Models: A Case Study of Reduced Precision and Machine Learning in the Gulf of Mexico -- Massively parallel EEG algorithms for pre-exascale architectures -- Online Job Failure Prediction in an HPC System -- Transitioning to Smart Sustainable Cities Based on Cutting-Edge Technological Improvements -- Algorithm Selection of MPI Collectives Considering System Utilization -- Service Management in Dynamic Edge Environments -- Path Plan Optimisation for UAV Assisted Data Collection in Large Areas -- Efficiently Distributed Federated Learning.</tableOfContents>
  <note type="statement of responsibility">edited by Demetris Zeinalipour, Dora Blanco Heras, George Pallis, Herodotos Herodotou, Demetris Trihinas, Daniel Balouek, Patrick Diehl, Terry Cojean, Karl Fürlinger, Maja Hanne Kirkeby, Matteo Nardelli, Pierangelo Di Sanzo.</note>
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    <topic>Computer networks </topic>
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    <topic>Computer Engineering and Networks</topic>
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  <subject>
    <topic>Operating Systems</topic>
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