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Euro-Par 2021: Parallel Processing

27th International Conference on Parallel and Distributed Computing, Lisbon, Portugal, September 1–3, 2021, Proceedings

  • Conference proceedings
  • © 2021

Overview

Editors:
  1. Leonel Sousa

    1. Universidade de Lisboa, Lisbon, Portugal

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  2. Nuno Roma

    1. Universidade de Lisboa, Lisbon, Portugal

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  3. Pedro Tomás

    1. Universidade de Lisboa, Lisbon, Portugal

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12820)

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

Included in the following conference series:

Conference proceedings info: Euro-Par 2021.

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About this book

This book constitutes the proceedings of the 27th International Conference on Parallel and Distributed Computing, Euro-Par 2021, held in Lisbon, Portugal, in August 2021. The conference was held virtually due to the COVID-19 pandemic.

The 38 full papers presented in this volume were carefully reviewed and selected from 136 submissions. They deal with parallel and distributed computing in general, focusing on compilers, tools and environments; performance and power modeling, prediction and evaluation; scheduling and load balancing; 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; parallel numerical methods and applications; and high performance architecture and accelerators.

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Table of contents (38 papers)

  1. Front Matter

    Pages i-xxxviii
    Download chapter PDF

  2. Compilers, Tools and Environments

    1. Front Matter

      Pages 1-1
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    2. ALONA: Automatic Loop Nest Approximation with Reconstruction and Space Pruning

      • Daniel Maier, Biagio Cosenza, Ben Juurlink
      Pages 3-18

    3. Automatic Low-Overhead Load-Imbalance Detection in MPI Applications

      • Peter Arzt, Yannic Fischler, Jan-Patrick Lehr, Christian Bischof
      Pages 19-34

  3. Performance and Power Modeling, Prediction and Evaluation

    1. Front Matter

      Pages 35-35
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    2. Trace-Based Workload Generation and Execution

      • Yannis Sfakianakis, Eleni Kanellou, Manolis Marazakis, Angelos Bilas
      Pages 37-54

    3. Update on the Asymptotic Optimality of LPT

      • Anne Benoit, Louis-Claude Canon, Redouane Elghazi, Pierre-Cyrille Héam
      Pages 55-69

    4. E2EWatch: An End-to-End Anomaly Diagnosis Framework for Production HPC Systems

      • Burak Aksar, Benjamin Schwaller, Omar Aaziz, Vitus J. Leung, Jim Brandt, Manuel Egele et al.
      Pages 70-85

  4. Scheduling and Load Balancing

    1. Front Matter

      Pages 87-87
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    2. Collaborative GPU Preemption via Spatial Multitasking for Efficient GPU Sharing

      • Zhuoran Ji, Cho-Li Wang
      Pages 89-104

    3. A Fixed-Parameter Algorithm for Scheduling Unit Dependent Tasks with Unit Communication Delays

      • Ning Tang, Alix Munier Kordon
      Pages 105-119

    4. Plan-Based Job Scheduling for Supercomputers with Shared Burst Buffers

      • Jan Kopanski, Krzysztof Rzadca
      Pages 120-135

    5. Taming Tail Latency in Key-Value Stores: A Scheduling Perspective

      • Sonia Ben Mokhtar, Louis-Claude Canon, Anthony Dugois, Loris Marchal, Etienne Rivière
      Pages 136-150

    6. A Log-Linear \((2 +5/6)\)-Approximation Algorithm for Parallel Machine Scheduling with a Single Orthogonal Resource

      • Adrian Naruszko, Bartłomiej Przybylski, Krzysztof Rządca
      Pages 151-166

    7. An MPI-based Algorithm for Mapping Complex Networks onto Hierarchical Architectures

      • Maria Predari, Charilaos Tzovas, Christian Schulz, Henning Meyerhenke
      Pages 167-182

    8. Pipelined Model Parallelism: Complexity Results and Memory Considerations

      • Olivier Beaumont, Lionel Eyraud-Dubois, Alena Shilova
      Pages 183-198

  5. Data Management, Analytics and Machine Learning

    1. Front Matter

      Pages 199-199
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    2. Efficient and Systematic Partitioning of Large and Deep Neural Networks for Parallelization

      • Haoran Wang, Chong Li, Thibaut Tachon, Hongxing Wang, Sheng Yang, Sébastien Limet et al.
      Pages 201-216

    3. A GPU Architecture Aware Fine-Grain Pruning Technique for Deep Neural Networks

      • Kyusik Choi, Hoeseok Yang
      Pages 217-231

    4. Towards Flexible and Compiler-Friendly Layer Fusion for CNNs on Multicore CPUs

      • Zhongyi Lin, Evangelos Georganas, John D. Owens
      Pages 232-248
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Other volumes

  1. Euro-Par 2021: Parallel Processing

  2. Euro-Par 2021: Parallel Processing Workshops

Editors and Affiliations

  • Universidade de Lisboa, Lisbon, Portugal

    Leonel Sousa, Nuno Roma, Pedro Tomás

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