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E-raamat: Languages and Compilers for Parallel Computing: 33rd International Workshop, LCPC 2020, Virtual Event, October 14-16, 2020, Revised Selected Papers

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Languages and Compilers for Parallel Computing: 33rd International Workshop, LCPC 2020, Virtual Event, October 14-16, 2020, Revised Selected PapersSuurem pilt
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This book constitutes the thoroughly refereed post-conference proceedings of the 33rd International Workshop on Languages and Compilers for Parallel Computing, LCPC 2020, held in Stony Brook, NY, USA, in October 2020. Due to COVID-19 pandemic the conference was held virtually. The 15 revised full papers were carefully reviewed and selected from 19 submissions. The contributions were organized in topical sections named as follows: Code and Data Transformations; OpenMP and Fortran; Domain Specific Compilation; Machine Language and Quantum Computing; Performance Analysis; Code Generation.
Code and Data Transformations An Affine Scheduling Framework for
Integrating Data Layout and Loop Transformations.- Guiding Code Optimizations
with Deep Learning-Based Code Matching.- Expanding Opportunities for Array
Privatization in Sparse Computations.- OpenMP and Fortran Concurrent
Execution of Deferred OpenMP Target Tasks with Hidden Helper Threads.- Using
Hardware Transactional Memory to Implement Speculative Privatization in
OpenMP.- Improving Fortran Performance Portability.- Domain Specific
Compilation COMET: A Domain-Specic Compilation of High-Performance
Computational Chemistry.-  G-Code Re-compilation and Optimization for Faster
3D Printing.- Li Machine Language and Quantum Computing Optimized Code
Generation for Deep Neural Networks.- Thermal-Aware Compilation of Spiking
Neural Networks to Neuromorphic Hardware.- A Quantum-Inspired Model For
Bit-Serial SIMD-Parallel Computation.- Performance Analysis Enhancing the
Top-Down Microarchitectural Analysis Method Using Purchasing Power Parity
Theory.- Code Generation Cain: Automatic Code Generation for Simultaneous
Convolutional Kernels on Focal-plane Sensor-processors.- Reordering Under the
ECMAScript Memory Consistency Model.- Verication of Vectorization of Signal
Transforms.
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