Languages and Compilers for Parallel Computing

32nd International Workshop, LCPC 2019, Atlanta, GA, USA, October 22¿24, 2019, Revised Selected Papers
Herausgegeben:Pande, Santosh; Sarkar, Vivek

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Theoretical Computer Science and General Issues 11998
• Verlag: Springer / Springer International Publishing / Springer, Berlin
• Artikelnr. des Verlages: 978-3-030-72788-8
• 1st ed. 2021
• Seitenzahl: 180
• Erscheinungstermin: 26. März 2021
• Englisch
• Abmessung: 235mm x 155mm x 11mm
• Gewicht: 283g
• ISBN-13: 9783030727888
• ISBN-10: 3030727882
• Artikelnr.: 61238769

Inhaltsangabe

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.