Latest News
OpenMP 5.0 Spec Now Available on Amazon
The OpenMP 5.0 Specification is now available as a softcover book on Amazon. 
MHPCC and the U of Manchester Join the OpenMP Effort
The Maui High-Performance Computing Center (MHPCC) and the University of Manchester have joined the OpenMP ARB. This brings the number of vendors and research organizations now collaborating on developing the standard parallel programming model to 33.
OPENMP 5.0 IS A MAJOR LEAP FORWARD
SC18, Dallas, Texas – November 8, 2018 – The OpenMP® Architecture Review Board (ARB) is pleased to announce Version 5.0 of the OpenMP API Specification, a major upgrade of the OpenMP language. The OpenMP community has made many requests since version 4.5 was introduced in 2015. As a result OpenMP 5.0 adds many new features that will be useful for highly parallel and complex applications. OpenMP now covers the entire hardware spectrum from embedded and accelerator devices to multicore systems with shared-memory. Vendors have made reference implementations of parts of the standard, and user courses will soon be given at OpenMP workshops and major conferences. More...
OpenMP @ SC18 Dallas, TX.
Join us in Dallas, TX. for SuperComputing 2018 where we will be celebrating the latest OpenMP 5.0 specification. In addition we have a packed agenda of other OpenMP events, inc. booth talks, BOF, tutorials, and papers. more
SUSE and University of Delaware Join the OpenMP Effort
SUSE and the University of Delaware have joined the OpenMP ARB, a group of leading hardware and software vendors and research organizations creating the standard for the most popular shared-memory parallel programming model in use today.
IEEE Article: The Ongoing Evolution of OpenMP
This paper presents an overview of the past, present and future of the OpenMP application programming interface (API). While the API originally specified a small set of directives that guided shared memory fork-join parallelization of loops and program sections, OpenMP now provides a richer set of directives that capture a wide range of parallelization strategies that are not strictly limited to shared memory.
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