Languages and Compilers for Parallel Computing
32nd International Workshop, LCPC 2019, Atlanta, GA, USA, October 22-24, 2019, Revised Selected Papers
Published on 25. March 2021
XII, 165 pages
978-3-030-72789-5 (ISBN)
Description
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.
More details
Series
Edition
1st ed. 2021
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Product notice
Reflowable
Illustrations
XII, 165 p. 71 illus., 47 illus. in color.
File size
14,49 MB
ISBN-13
978-3-030-72789-5 (9783030727895)
DOI
10.1007/978-3-030-72789-5
Schweitzer Classification
Other editions
Additional editions
Santosh Pande | Vivek Sarkar
Languages and Compilers for Parallel Computing
32nd International Workshop, LCPC 2019, Atlanta, GA, USA, October 22-24, 2019, Revised Selected Papers
Book
03/2021
Springer
€53.49
Shipment within 7-9 days
Content
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.
