This book constitutes the proceedings of the 13th International Workshop on OpenMP, IWOMP 2017, held in Stony Brook, NY, USA, in September 2017. The 23 full papers presented in this volume were carefully reviewed and selected from 28 submissions. They were organized in topical sections named: Advanced Implementations and Extensions; OpenMP Application Studies; Analyzing and Extending Tasking; OpenMP 4 Application Evaluation; Extended Parallelism Models: Performance Analysis and Tools; and Advanced Data Management with OpenMP.

This book constitutes the refereed proceedings of the 11th International Workshop on OpenMP, held in Aachen, Germany, in October 2015. The 19 technical full papers presented were carefully reviewed and selected from 22 submissions. The papers are organized in topical sections on applications, accelerator applications, tools, extensions, compiler and runtime, and energy.

A guide to the most recent, advanced features of the widely used OpenMP parallel programming model, with coverage of major features in OpenMP 4.5. This book offers an up-to-date, practical tutorial on advanced features in the widely used OpenMP parallel programming model. Building on the previous volume, Using OpenMP: Portable Shared Memory Parallel Programming (MIT Press), this book goes beyond the fundamentals to focus on what has been changed and added to OpenMP since the 2.5 specifications. It emphasizes four major and advanced areas: thread affinity (keeping threads close to their data), accelerators (special hardware to speed up certain operations), tasking (to parallelize algorithms with a less regular execution flow), and SIMD (hardware assisted operations on vectors). As in the earlier volume, the focus is on practical usage, with major new features primarily introduced by example. Examples are restricted to C and C++, but are straightforward enough to be understood by Fortran programmers. After a brief recap of OpenMP 2.5, the book reviews enhancements introduced since 2.5. It then discusses in detail tasking, a major functionality enhancement; Non-Uniform Memory Access (NUMA) architectures, supported by OpenMP; SIMD, or Single Instruction Multiple Data; heterogeneous systems, a new parallel programming model to offload computation to accelerators; and the expected further development of OpenMP.