Languages and Compilers for Parallel Computing

1. Front Matter

   Pages I-IX

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2. Accelerator Programming

   1. Front Matter

      Pages 1-1

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   2. Optimistic Parallelism on GPUs

      • Min Feng, Rajiv Gupta, Laxmi N. Bhuyan

      Pages 3-18

   3. Directive-Based Compilers for GPUs

      • Swapnil Ghike, Rubén Gran, María J. Garzarán, David Padua

      Pages 19-35

   4. GLES: A Practical GPGPU Optimizing Compiler Using Data Sharing and Thread Coarsening

      • Zhen Lin, Xiaopeng Gao, Han Wan, Bo Jiang

      Pages 36-50

   5. Evaluating Performance Portability of OpenACC

      • Amit Sabne, Putt Sakdhnagool, Seyong Lee, Jeffrey S. Vetter

      Pages 51-66

   6. NAS Parallel Benchmarks for GPGPUs Using a Directive-Based Programming Model

      • Rengan Xu, Xiaonan Tian, Sunita Chandrasekaran, Yonghong Yan, Barbara Chapman

      Pages 67-81

   7. Understanding Co-run Degradations on Integrated Heterogeneous Processors

      • Qi Zhu, Bo Wu, Xipeng Shen, Li Shen, Zhiying Wang

      Pages 82-97

3. Algorithms for Parallelism

   1. Front Matter

      Pages 99-99

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   2. Simultaneous Inspection: Hiding the Overhead of Inspector-Executor Style Dynamic Parallelization

      • Daniel Brinkers, Ronald Veldema, Michael Philippsen

      Pages 101-115

   3. Tiled Linear Algebra a System for Parallel Graph Algorithms

      • Saeed Maleki, G. Carl Evans, David A. Padua

      Pages 116-130

   4. An Approach for Proving the Correctness of Inspector/Executor Transformations

      • Michael Norrish, Michelle Mills Strout

      Pages 131-145

   5. Fast Automatic Heuristic Construction Using Active Learning

      • William F. Ogilvie, Pavlos Petoumenos, Zheng Wang, Hugh Leather

      Pages 146-160

   6. Jagged Tiling for Intra-tile Parallelism and Fine-Grain Multithreading

      • Sunil Shrestha, Joseph Manzano, Andres Marquez, John Feo, Guang R. Gao

      Pages 161-175

   7. The stapl Skeleton Framework

      • Mani Zandifar, Nathan Thomas, Nancy M. Amato, Lawrence Rauchwerger

      Pages 176-190

4. Compilers

   1. Front Matter

      Pages 191-191

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   2. Memory Management Techniques for Exploiting RDMA in PGAS Languages

      • Barnaby Dalton, Gabriel Tanase, Michail Alvanos, Gheorghe Almási, Ettore Tiotto

      Pages 193-207

   3. Change Detection Based Parallelism Mapping: Exploiting Offline Models and Online Adaptation

      • Murali Krishna Emani, Michael O’Boyle

      Pages 208-223

   4. Automatic Streamization of Image Processing Applications

      • Pierre Guillou, Fabien Coelho, François Irigoin

      Pages 224-238

   5. Evaluation of Automatic Power Reduction with OSCAR Compiler on Intel Haswell and ARM Cortex-A9 Multicores

      • Tomohiro Hirano, Hideo Yamamoto, Shuhei Iizuka, Kohei Muto, Takashi Goto, Tamami Wake et al.

      Pages 239-252

This book constitutes the thoroughly refereed post-conference proceedings of the 27th International Workshop on Languages and Compilers for Parallel Computing, LCPC 2014, held in Hillsboro, OR, USA, in September 2014. The 25 revised full papers were carefully reviewed and selected from 39 submissions. The papers are organized in topical sections on accelerator programming; algorithms for parallelism; compilers; debugging; vectorization.

• Automatic parallelization
• Compilers
• Debugging
• GPGPU
• Graph algorithms
• Heterogeneous architecture
• High performance computing
• Machine learning
• Message passing
• Multicore processor
• Multithreading
• Parallel algorithms
• Parallel loops
• Parallel programming languages
• Performance optimization
• Power reduction
• Runtime environments
• Vectorization
• Workload scheduling
• optimization

• Intel Corporation, Santa Clara, USA

  James Brodman, Peng Tu

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