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Euro-Par 2023: Parallel Processing

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Table of Contents

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    Book Overview
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    Chapter 1 DIPPM: A Deep Learning Inference Performance Predictive Model Using Graph Neural Networks
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    Chapter 2 perun : Benchmarking Energy Consumption of High-Performance Computing Applications
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    Chapter 3 Extending OpenSHMEM with Aggregation Support for Improved Message Rate Performance
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    Chapter 4 Fault-Aware Group-Collective Communication Creation and Repair in MPI
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    Chapter 5 MetaLive: Meta-Reinforcement Learning Based Collective Bitrate Adaptation for Multi-Party Live Streaming
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    Chapter 6 Asymptotic Performance and Energy Consumption of SLACK
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    Chapter 7 A Poisson-Based Approximation Algorithm for Stochastic Bin Packing of Bernoulli Items
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    Chapter 8 Hierarchical Management of Extreme-Scale Task-Based Applications
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    Chapter 9 MESDD: A Distributed Geofence-Based Discovery Method for the Computing Continuum
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    Chapter 10 Parameterized Analysis of a Dynamic Programming Algorithm for a Parallel Machine Scheduling Problem
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    Chapter 11 SparkEdgeEmu: An Emulation Framework for Edge-Enabled Apache Spark Deployments
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    Chapter 12 ODIN: Overcoming Dynamic Interference in iNference Pipelines
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    Chapter 13 DAG-Based Efficient Parallel Scheduler for Blockchains: Hyperledger Sawtooth as a Case Study
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    Chapter 14 INSTANT: A Runtime Framework to Orchestrate In-Situ Workflows
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    Chapter 15 How Do OS and Application Schedulers Interact? An Investigation with Multithreaded Applications
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    Chapter 16 Assessing Power Needs to Run a Workload with Quality of Service on Green Datacenters
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    Chapter 17 Improving Utilization of Dataflow Architectures Through Software and Hardware Co-Design
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    Chapter 18 A Multi-level Parallel Integer/Floating-Point Arithmetic Architecture for Deep Learning Instructions
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    Chapter 19 Lock-Free Bucketized Cuckoo Hashing
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    Chapter 20 BitHist: A Precision-Scalable Sparse-Awareness DNN Accelerator Based on  Bit Slices Products Hist ogram
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    Chapter 21 Computational Storage for an Energy-Efficient Deep Neural Network Training System
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    Chapter 22 Optimizing Data Movement for GPU-Based In-Situ Workflow Using GPUDirect RDMA
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    Chapter 23 FedGM: Heterogeneous Federated Learning via Generative Learning and Mutual Distillation
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    Chapter 24 DeTAR: A Decision Tree-Based Adaptive Routing in Networks-on-Chip
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    Chapter 25 Auto-Divide GNN: Accelerating GNN Training with Subgraph Division
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    Chapter 26 Model-Agnostic Federated Learning
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    Chapter 27 Scalable Random Forest with Data-Parallel Computing
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    Chapter 28 SymED: Adaptive and Online Symbolic Representation of Data on the Edge
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    Chapter 29 MMExit: Enabling Fast and Efficient Multi-modal DNN Inference with Adaptive Network Exits
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    Chapter 30 Distributed Deep Multilevel Graph Partitioning
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    Chapter 31 TrainBF: High-Performance DNN Training Engine Using BFloat16 on AI Accelerators
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    Chapter 32 Distributed k-Means with Outliers in General Metrics
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    Chapter 33 A Parallel Scan Algorithm in the Tensor Core Unit Model
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    Chapter 34 Improved Algorithms for Monotone Moldable Job Scheduling Using Compression and Convolution
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    Chapter 35 On Size Hiding Protocols in Beeping Model
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    Chapter 36 Efficient Protective Jamming in 2D SINR Networks
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    Chapter 37 GPU Code Generation of Cardiac Electrophysiology Simulation with MLIR
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    Chapter 38 SWSPH: A Massively Parallel SPH Implementation for Hundred-Billion-Particle Simulation on New Sunway Supercomputer
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    Chapter 39 Transactional-Turn Causal Consistency
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    Chapter 40 Im2win: An Efficient Convolution Paradigm on GPU
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    Chapter 41 Accelerating Drug Discovery in AutoDock-GPU with Tensor Cores
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    Chapter 42 FedCML: Federated Clustering Mutual Learning with non-IID Data
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    Chapter 43 A Look at Performance and Scalability of the GPU Accelerated Sparse Linear System Solver Spliss
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    Chapter 44 Parareal with a Physics-Informed Neural Network as Coarse Propagator
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    Chapter 45 Faster Segmented Sort on GPUs
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    Chapter 46 Hercules: Scalable and Network Portable In-Memory Ad-Hoc File System for Data-Centric and High-Performance Applications
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    Chapter 47 An Efficient Parallel Adaptive GMG Solver for Large-Scale Stokes Problems
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    Chapter 48 Optimizing Distributed Tensor Contractions Using Node-Aware Processor Grids
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    Chapter 49 Parallel Cholesky Factorization for Banded Matrices Using OpenMP Tasks
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Title
Euro-Par 2023: Parallel Processing
Published by
Springer Nature Switzerland, September 2023
DOI 10.1007/978-3-031-39698-4
ISBNs
978-3-03-139697-7, 978-3-03-139698-4
Editors

Cano, José, Dikaiakos, Marios D., Papadopoulos, George A., Pericàs, Miquel, Sakellariou, Rizos

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