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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

Medium: Buch
ISBN: 978-3-030-72788-8
Verlag: Palgrave Macmillan
Erscheinungstermin: 26.03.2021
Lieferfrist: bis zu 10 Tage

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.


Produkteigenschaften


  • Artikelnummer: 9783030727888
  • Medium: Buch
  • ISBN: 978-3-030-72788-8
  • Verlag: Palgrave Macmillan
  • Erscheinungstermin: 26.03.2021
  • Sprache(n): Englisch
  • Auflage: 1. Auflage 2021
  • Serie: Lecture Notes in Computer Science
  • Produktform: Kartoniert, Paperback
  • Gewicht: 283 g
  • Seiten: 165
  • Format (B x H x T): 155 x 235 x 11 mm
  • Ausgabetyp: Kein, Unbekannt
Autoren/Hrsg.

Herausgeber

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.