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Euro-Par 2020: Parallel Processing

Overview of attention for book
Euro-Par 2020: Parallel Processing
Springer International Publishing

Table of Contents

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    Book Overview
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    Chapter 1 Skipping Non-essential Instructions Makes Data-Dependence Profiling Faster
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    Chapter 2 A Toolchain to Verify the Parallelization of OmpSs-2 Applications
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    Chapter 3 Towards a Model to Estimate the Reliability of Large-Scale Hybrid Supercomputers
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    Chapter 4 A Learning-Based Approach for Evaluating the Capacity of Data Processing Pipelines
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    Chapter 5 Operation-Aware Power Capping
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    Chapter 6 A Comparison of the Scalability of OpenMP Implementations
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    Chapter 7 Evaluating the Effectiveness of a Vector-Length-Agnostic Instruction Set
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    Chapter 8 Parallel Scheduling of Data-Intensive Tasks
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    Chapter 9 A Makespan Lower Bound for the Tiled Cholesky Factorization Based on ALAP Schedule
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    Chapter 10 Optimal GPU-CPU Offloading Strategies for Deep Neural Network Training
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    Chapter 11 Improving Mapping for Sparse Direct Solvers
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    Chapter 12 Modelling Standard and Randomized Slimmed Folded Clos Networks
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    Chapter 13 OmpMemOpt: Optimized Memory Movement for Heterogeneous Computing
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    Chapter 14 Accelerating Deep Learning Inference with Cross-Layer Data Reuse on GPUs
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    Chapter 15 Distributed Fine-Grained Traffic Speed Prediction for Large-Scale Transportation Networks Based on Automatic LSTM Customization and Sharing
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    Chapter 16 Optimizing FFT-Based Convolution on ARMv8 Multi-core CPUs
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    Chapter 17 Maximizing I/O Bandwidth for Reverse Time Migration on Heterogeneous Large-Scale Systems
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    Chapter 18 TorqueDB: Distributed Querying of Time-Series Data from Edge-local Storage
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    Chapter 19 Data-Centric Distributed Computing on Networks of Mobile Devices
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    Chapter 20 WPSP: A Multi-correlated Weighted Policy for VM Selection and Migration for Cloud Computing
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    Chapter 21 LCP-Aware Parallel String Sorting
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    Chapter 22 Mobile RAM and Shape Formation by Programmable Particles
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    Chapter 23 Approximation Algorithm for Estimating Distances in Distributed Virtual Environments
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    Chapter 24 On the Power of Randomization in Distributed Algorithms in Dynamic Networks with Adaptive Adversaries
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    Chapter 25 3D Coded SUMMA: Communication-Efficient and Robust Parallel Matrix Multiplication
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    Chapter 26 Managing Failures in Task-Based Parallel Workflows in Distributed Computing Environments
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    Chapter 27 Accelerating Nested Data Parallelism: Preserving Regularity
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    Chapter 28 Using Dynamic Broadcasts to Improve Task-Based Runtime Performances
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    Chapter 29 A Compression-Based Design for Higher Throughput in a Lock-Free Hash Map
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    Chapter 30 NV-PhTM: An Efficient Phase-Based Transactional System for Non-volatile Memory
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    Chapter 31 Enhancing Resource Management Through Prediction-Based Policies
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    Chapter 32 Accelerating Overlapping Community Detection: Performance Tuning a Stochastic Gradient Markov Chain Monte Carlo Algorithm
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    Chapter 33 A Prediction Framework for Fast Sparse Triangular Solves
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    Chapter 34 Multiprecision Block-Jacobi for Iterative Triangular Solves
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    Chapter 35 Efficient Ephemeris Models for Spacecraft Trajectory Simulations on GPUs
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    Chapter 36 Parallel Finite Cell Method with Adaptive Geometric Multigrid
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    Chapter 37 cuDTW++: Ultra-Fast Dynamic Time Warping on CUDA-Enabled GPUs
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    Chapter 38 Heterogeneous CPU+iGPU Processing for Efficient Epistasis Detection
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    Chapter 39 SYCL-Bench: A Versatile Cross-Platform Benchmark Suite for Heterogeneous Computing
Attention for Chapter 17: Maximizing I/O Bandwidth for Reverse Time Migration on Heterogeneous Large-Scale Systems
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Chapter title
Maximizing I/O Bandwidth for Reverse Time Migration on Heterogeneous Large-Scale Systems
Chapter number 17
Book title
Euro-Par 2020: Parallel Processing
Published by
Springer, Cham, August 2020
DOI 10.1007/978-3-030-57675-2_17
Book ISBNs
978-3-03-057674-5, 978-3-03-057675-2
Authors

Tariq Alturkestani, Hatem Ltaief, David Keyes, Alturkestani, Tariq, Ltaief, Hatem, Keyes, David

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 40%
Other 1 20%
Unknown 2 40%
Readers by discipline Count As %
Computer Science 3 60%
Social Sciences 1 20%
Unknown 1 20%