Languages and Compilers for Parallel Computing

33rd International Workshop, LCPC 2020, Virtual Event, October 14-16, 2020, Revised Selected Papers

AvBarbara Chapman,José Moreira

Häftad, Engelska, 2022

Del 13149 i serien Lecture Notes in Computer Science

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Beskrivning

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. The contributions were organized in topical sections named as follows: Code and Data Transformations;

Produktinformation

Utgivningsdatum:2022-02-16
Mått:155 x 235 x 14 mm
Vikt:376 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:233
Förlag:Springer Nature Switzerland AG
ISBN:9783030959524

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Innehållsförteckning

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.