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Euro-Par 2023: Parallel Processing Workshops Euro-Par 2023 International Workshops, Limassol, Cyprus, August 28 - September 1, 2023, Revised Selected Papers, Part II
Title
Euro-Par 2023: Parallel Processing Workshops [electronic resource] : Euro-Par 2023 International Workshops, Limassol, Cyprus, August 28 - September 1, 2023, Revised Selected Papers, Part II / 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.
ISBN
9783031488030
Edition
1st ed. 2024.
Publication
Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
Physical Description
1 online resource (XV, 337 p.) 95 illus., 76 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
This book constitutes revised selected papers from the workshops held at the 29th International Conference on Parallel and Distributed Computing, Euro-Par 2023, which took place in Limassol, Cyprus, during August 28-September 1, 2023. The 42 full papers presented in this book together with 11 symposium papers and 14 demo/poster papers were carefully reviewed and selected from 55 submissions. The papers cover covering all aspects of parallel and distributed processing, ranging from theory to practice, from small to the largest parallel and distributed systems and infrastructures, from fundamental computational problems to applications, from architecture, compiler, language and interface design and implementation, to tools, support infrastructures, and application performance aspects. LNCS 14351: First International Workshop on Scalable Compute Continuum (WSCC 2023). First International Workshop on Tools for Data Locality, Power and Performance (TDLPP 2023). First International Workshop on Urgent Analytics for Distributed Computing (QuickPar 2023). 21st International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Platforms (HETEROPAR 2023). LNCS 14352: Second International Workshop on Resource AWareness of Systems and Society (RAW 2023). Third International Workshop on Asynchronous Many-Task systems for Exascale (AMTE 2023). Third International Workshop on Performance and Energy-efficiency in Concurrent and Distributed Systems (PECS 2023) First Minisymposium on Applications and Benefits of UPMEM commercial Massively Parallel Processing-In-Memory Platform (ABUMPIMP 2023). First Minsymposium on Adaptive High Performance Input / Output Systems (ADAPIO 2023). .
Variant and related titles
Springer ENIN.
Other formats
Printed edition:
Printed edition:
Format
Books / Online
Language
English
Added to Catalog
May 08, 2024
Series
Lecture Notes in Computer Science, 14352
Contents
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.
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