Buch, Englisch, 233 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 376 g
33rd International Workshop, LCPC 2020, Virtual Event, October 14-16, 2020, Revised Selected Papers
Buch, Englisch, 233 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 376 g
Reihe: Theoretical Computer Science and General Issues
ISBN: 978-3-030-95952-4
Verlag: Springer International Publishing
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.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
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.
