Languages and Compilers for Parallel Computing

Table of Contents

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   Book Overview
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   Chapter 1 Optimistic Parallelism on GPUs
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   Chapter 2 Directive-Based Compilers for GPUs
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   Chapter 3 GLES: A Practical GPGPU Optimizing Compiler Using Data Sharing and Thread Coarsening
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   Chapter 4 Evaluating Performance Portability of OpenACC
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   Chapter 5 NAS Parallel Benchmarks for GPGPUs Using a Directive-Based Programming Model
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   Chapter 6 Understanding Co-run Degradations on Integrated Heterogeneous Processors
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   Chapter 7 Simultaneous Inspection: Hiding the Overhead of Inspector-Executor Style Dynamic Parallelization
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   Chapter 8 Tiled Linear Algebra a System for Parallel Graph Algorithms
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    Chapter 9 An Approach for Proving the Correctness of Inspector/Executor Transformations
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    Chapter 10 Fast Automatic Heuristic Construction Using Active Learning
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    Chapter 11 Jagged Tiling for Intra-tile Parallelism and Fine-Grain Multithreading
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    Chapter 12 Languages and Compilers for Parallel Computing
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    Chapter 13 Memory Management Techniques for Exploiting RDMA in PGAS Languages
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    Chapter 14 Change Detection Based Parallelism Mapping: Exploiting Offline Models and Online Adaptation
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    Chapter 15 Automatic Streamization of Image Processing Applications
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    Chapter 16 Evaluation of Automatic Power Reduction with OSCAR Compiler on Intel Haswell and ARM Cortex-A9 Multicores
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    Chapter 17 \(\pi \) Abstraction: Parallelism-Aware Array Data Flow Analysis for OpenMP
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    Chapter 18 Static Approximation of MPI Communication Graphs for Optimized Process Placement
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    Chapter 19 Automatic Parallelism Through Macro Dataflow in MATLAB
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    Chapter 20 Re-Engineering Compiler Transformations to Outperform Database Query Optimizers
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    Chapter 21 Systematic Debugging of Concurrent Systems Using Coalesced Stack Trace Graphs
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    Chapter 22 LightPlay: Efficient Replay with GPUs
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    Chapter 23 Exploring and Evaluating Array Layout Restructuring for SIMDization
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    Chapter 24 Unification of Static and Dynamic Analyses to Enable Vectorization
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    Chapter 25 Efficient Exploitation of Hyper Loop Parallelism in Vectorization