Languages and Compilers for Parallel Computing, 1st ed. 2019
31st International Workshop, LCPC 2018, Salt Lake City, UT, USA, October 9-11, 2018, Revised Selected Papers
Theoretical Computer Science and General Issues Series

Coordinators: Hall Mary, Sundar Hari

Language: English

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This book constitutes the thoroughly refereed post-conference proceedings of the 31st International Workshop on Languages and Compilers for Parallel Computing, LCPC 2018, held in Salt Lake City, UT, USA, in October 2018. The 14 revised full papers were carefully reviewed and selected from 26 submissions. Specific topics are compiling for parallelism and parallel compilers, static, dynamic, and adaptive optimization of parallel programs, parallel programming models and languages, formal analysis and verification of parallel programs, parallel runtime systems and libraries, performance analysis and debugging tools for concurrency and parallelism, parallel algorithms and concurrent data structures, parallel applications, synchronization and concurrency control, software engineering for parallel programs, fault tolerance for parallel systems, and parallel programming and compiling for heterogeneous systems.

A Unified Approach to Variable Renaming for Enhanced Vectorization.- Design and Performance Analysis of Real-time Dynamic Streaming Applications.- A Similarity Measure for GPU Kernel Subgraph Matching.- New Opportunities for Compilers in Computer Security.- Footmark: A New Working Set Definition.- Towards an Achievable Performance for the Loop Nests.- Extending Index-Array Properties for Data Dependence Analysis.- Optimized Sound and Complete Data Race Detection in Structured Parallel Programs.- Compiler Optimizations for Parallel Programs.- MATE, a Unified Model for Communication-Tolerant Scientific Applications.- GASNet-EX: A High-Performance, Portable Communication Library for Exascale.- Nested Parallelism with Algorithmic Skeletons.- HDArray: Parallel Array Interface for Distributed Heterogeneous Devices.- Automating the Exchangeability of Shared Data Abstractions.