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Euro-Par 2020: Parallel Processing

26th International Conference on Parallel and Distributed Computing, Warsaw, Poland, August 24–28, 2020, Proceedings

  • Conference proceedings
  • © 2020

Overview

Editors:
  1. Maciej Malawski

    1. AGH University of Science and Technology, Krakow, Poland

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  2. Krzysztof Rzadca

    1. University of Warsaw, Warsaw, Poland

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12247)

Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)

Included in the following conference series:

Conference proceedings info: Euro-Par 2020.

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Table of contents (39 papers)

  1. Front Matter

    Pages i-xliii
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  2. Support Tools and Environments

    1. Front Matter

      Pages 1-1
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    2. Skipping Non-essential Instructions Makes Data-Dependence Profiling Faster

      • Nicolas Morew, Mohammad Norouzi, Ali Jannesari, Felix Wolf
      Pages 3-17

    3. A Toolchain to Verify the Parallelization of OmpSs-2 Applications

      • Simone Economo, Sara Royuela, Eduard Ayguadé, Vicenç Beltran
      Pages 18-33

  3. Performance and Power Modeling, Prediction and Evaluation

    1. Front Matter

      Pages 35-35
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    2. Towards a Model to Estimate the Reliability of Large-Scale Hybrid Supercomputers

      • Elvis Rojas, Esteban Meneses, Terry Jones, Don Maxwell
      Pages 37-51

    3. A Learning-Based Approach for Evaluating the Capacity of Data Processing Pipelines

      • Maha Alsayasneh, Noel De Palma
      Pages 52-67

    4. Operation-Aware Power Capping

      • Bo Wang, Julian Miller, Christian Terboven, Matthias Müller
      Pages 68-82

    5. A Comparison of the Scalability of OpenMP Implementations

      • Tim Jammer, Christian Iwainsky, Christian Bischof
      Pages 83-97

    6. Evaluating the Effectiveness of a Vector-Length-Agnostic Instruction Set

      • Andrei Poenaru, Simon McIntosh-Smith
      Pages 98-114

  4. Scheduling and Load Balancing

    1. Front Matter

      Pages 115-115
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    2. Parallel Scheduling of Data-Intensive Tasks

      • Xiao Meng, Lukasz Golab
      Pages 117-133

    3. A Makespan Lower Bound for the Tiled Cholesky Factorization Based on ALAP Schedule

      • Olivier Beaumont, Julien Langou, Willy Quach, Alena Shilova
      Pages 134-150

    4. Optimal GPU-CPU Offloading Strategies for Deep Neural Network Training

      • Olivier Beaumont, Lionel Eyraud-Dubois, Alena Shilova
      Pages 151-166

    5. Improving Mapping for Sparse Direct Solvers

      • Changjiang Gou, Ali Al Zoobi, Anne Benoit, Mathieu Faverge, Loris Marchal, Grégoire Pichon et al.
      Pages 167-182

  5. High Performance Architectures and Compilers

    1. Front Matter

      Pages 183-183
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    2. Modelling Standard and Randomized Slimmed Folded Clos Networks

      • Cristóbal Camarero, Javier Corral, Carmen Martínez, Ramón Beivide
      Pages 185-199

    3. OmpMemOpt: Optimized Memory Movement for Heterogeneous Computing

      • Prithayan Barua, Jisheng Zhao, Vivek Sarkar
      Pages 200-216

  6. Data Management, Analytics and Machine Learning

    1. Front Matter

      Pages 217-217
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    2. Accelerating Deep Learning Inference with Cross-Layer Data Reuse on GPUs

      • Xueying Wang, Guangli Li, Xiao Dong, Jiansong Li, Lei Liu, Xiaobing Feng
      Pages 219-233
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Other volumes

  1. Euro-Par 2020: Parallel Processing

  2. Euro-Par 2020: Parallel Processing Workshops

Keywords

About this book

This book constitutes the proceedings of the 26th International Conference on Parallel and Distributed Computing, Euro-Par 2020, held in Warsaw, Poland, in August 2020. The conference was held virtually due to the coronavirus pandemic.

The 39 full papers presented in this volume were carefully reviewed and selected from 158 submissions. They deal with parallel and distributed computing in general, focusing on support tools and environments; performance and power modeling, prediction and evaluation; scheduling and load balancing; high performance architectures and compilers; data management, analytics and machine learning; cluster, cloud and edge computing; theory and algorithms for parallel and distributed processing; parallel and distributed programming, interfaces, and languages; multicore and manycore parallelism; parallel numerical methods and applications; and accelerator computing.

Editors and Affiliations

  • AGH University of Science and Technology, Krakow, Poland

    Maciej Malawski

  • University of Warsaw, Warsaw, Poland

    Krzysztof Rzadca

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