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Languages and Compilers for Parallel Computing

30th International Workshop, LCPC 2017, College Station, TX, USA, October 11–13, 2017, Revised Selected Papers

  • Conference proceedings
  • © 2019

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Editors:
  1. Lawrence Rauchwerger

    1. Texas A&M University, College Station, USA

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 11403)

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

Included in the following conference series:

Conference proceedings info: LCPC 2017.

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Table of contents (17 papers)

  1. Front Matter

    Pages i-xxxiv
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  2. Compilers for Parallel Computing

    1. Front Matter

      Pages 1-1
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    2. Using Hardware Counters to Predict Vectorization

      • Neftali Watkinson, Aniket Shivam, Zhi Chen, Alexander Veidenbaum, Alexandru Nicolau, Zhangxiaowen Gong
      Pages 3-16

    3. Software Cache Coherent Control by Parallelizing Compiler

      • Boma A. Adhi, Masayoshi Mase, Yuhei Hosokawa, Yohei Kishimoto, Taisuke Onishi, Hiroki Mikami et al.
      Pages 17-25

    4. Polyhedral Compilation Support for C++ Features: A Case Study with CPPTRAJ

      • Amit Roy, Daniel Roe, Mary Hall, Thomas Cheatham
      Pages 26-35

    5. Language-Agnostic Optimization and Parallelization for Interpreted Languages

      • Michelle Mills Strout, Saumya Debray, Kate Isaacs, Barbara Kreaseck, Julio Cárdenas-Rodríguez, Bonnie Hurwitz et al.
      Pages 36-46

  3. Performance Modeling and Instrumentation

    1. Front Matter

      Pages 47-47
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    2. Memory Distance Measurement for Concurrent Programs

      • Hao Li, Jialiang Chang, Zijiang Yang, Steve Carr
      Pages 49-64

    3. Efficient Cache Simulation for Affine Computations

      • Wenlei Bao, Prashant Singh Rawat, Martin Kong, Sriram Krishnamoorthy, Louis-Noel Pouchet, P. Sadayappan
      Pages 65-85

    4. ADLER: Adaptive Sampling for Precise Monitoring

      • Arnamoy Bhattacharyya, Cristiana Amza
      Pages 86-100

    5. How Low Can You Go?

      • Henry Dietz
      Pages 101-108

    6. Memory-Access-Pattern Analysis Techniques for OpenCL Kernels

      • Gangwon Jo, Jaehoon Jung, Jiyoung Park, Jaejin Lee
      Pages 109-126

  4. Parallel Programming and Abstractions

    1. Front Matter

      Pages 127-127
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    2. Characterizing Performance of Imbalanced Collectives on Hybrid and Task Centric Runtimes for Two-Phase Reduction

      • Udayanga Wickramasinghe, Andrew Lumsdaine
      Pages 129-144

    3. Abstract Representation of Shared Data for Heterogeneous Computing

      • Tushar Kumar, Aravind Natarajan, Wenjia Ruan, Mario Badr, Dario Suarez Gracia, Calin Cascaval
      Pages 145-162

    4. Parallel Roles for Practical Deterministic Parallel Programming

      • Michael Faes, Thomas R. Gross
      Pages 163-181

    5. Mozart : Efficient Composition of Library Functions for Heterogeneous Execution

      • Rajkishore Barik, Tatiana Shpeisman, Hongbo Rong, Chunling Hu, Victor W. Lee, Todd A. Anderson et al.
      Pages 182-202

    6. Lock-Free Transactional Adjacency List

      • Zachary Painter, Christina Peterson, Damian Dechev
      Pages 203-219

  5. GPU Applications

    1. Front Matter

      Pages 221-221
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    2. Efficient Inspected Critical Sections in Data-Parallel GPU Codes

      • Thorsten Blaß, Michael Philippsen, Ronald Veldema
      Pages 223-239
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Other volumes

  1. Languages and Compilers for Parallel Computing

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About this book

This book constitutes the proceedings of the 30th International Workshop on Languages and Compilers for Parallel Computing, LCPC 2017, held in College Station, TX, USA, in October 2017.


The 17 full papers presented together with abstracts of 5 keynote talks, 11 invited speakers and 4 poster papers in this volume were carefully reviewed and selected from 26 submissions. LCPC encourages submissions that go outside its original scope of scientific computing to diverse areas that are enable or enhanced by the power of parallel systems such as mobile computing, big data, relevant aspects of machine learning, data centers, cognitive computing, etc. LCPC strongly encourages personal interaction and technical discussions along the initial material.

Editors and Affiliations

  • Texas A&M University, College Station, USA

    Lawrence Rauchwerger

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