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Languages and Compilers for Parallel Computing: 32nd International Workshop, LCPC 2019, Atlanta, GA, USA, October 22–24, 2019, Revised Selected Papers: Lecture Notes in Computer Science, cartea 11998

Editat de Santosh Pande, Vivek Sarkar
en Limba Engleză Paperback – 26 mar 2021
This book constitutes the thoroughly refereed post-conference proceedings of the 32nd International Workshop on Languages and Compilers for Parallel Computing, LCPC 2019, held in Atlanta, GA, USA, in October 2019. The 8 revised full papers and 3 revised short papers were carefully reviewed and selected from 17 submissions. The scope of the workshop includes advances in programming systems for current domains and platforms, e.g., scientific computing, batch/ streaming/ real-time data analytics, machine learning, cognitive computing, heterogeneous/ reconfigurable computing, mobile computing, cloud computing, IoT, as well as forward-looking computing domains such as analog and quantum computing.
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Specificații

ISBN-13: 9783030727888
ISBN-10: 3030727882
Pagini: 400
Ilustrații: XII, 165 p. 71 illus., 47 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.26 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Theoretical Computer Science and General Issues

Locul publicării:Cham, Switzerland

Cuprins

Performance of Static and Dynamic Task Scheduling for Real-Time Engine Control System on Embedded Multicore Processor.- PostSLP: Cross-Region Vectorization of Fully or Partially Vectorized Code.- FLARE: Flexibly Sharing Commodity GPUs to Enforce QoS and Improve Utilization.- Foundations of consistency types for a higher-order distributed language.- Common Subexpression Convergence: A New Code Optimization for SIMT processors.- Using Performance Event Profiles to Deduce an Execution Model of MATLAB with Just-In-Time Compilation.- CLAM: Compiler Leasing of Accelerator Memory.- Abstractions for Polyhedral Topology-Aware Tasking.- SWIRL++: Evaluating Performance Models to Guide Code Transformation in Convolutional Neural Networks.- A Structured Grid Solver with Polyhedral+Dataflow Representation.- CubeGen: Code Generation for Accelerated GEMM-based Convolution with Tiling.