The University of Houston (UH) has joined the OpenMP Consortium, 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.
“We are excited to join the OpenMP family as an academic member”, says Barbara Chapman, Professor of Computer Science and director of the Center for Advanced Computing and Data Systems (CACDS) at UH. “Through cOMPunity, we have been involved since the beginning. With this new membership, we will continue to engage with the OpenMP community.”
“This is a great step forward for the UH HPCTools research group. We look forward to continuing our efforts to support standardization of OpenMP, to providing reference implementations through the OpenUH compiler and proposing new features”, says Yonghong Yan, Research Assistant Professor at UH.
The University of Houston is located in the Houston metropolitan area, world headquarters for the energy industry and home to the Texas Medical Center. UH HPCTools Research group, led by Chapman and Yan, performs research on parallel programming models, compiler technologies, and HPC applications and systems. CACDS provides HPC resources at UH.
“I wish to warmly welcome UH to OpenMP, although they are no stranger, with a rich and deep history of working with and contributing to OpenMP as well as research regarding OpenMP” says Michael Wong, OpenMP CEO.
The OpenMP ARB now has 14 permanent members and 12 auxiliary members. Permanent members are vendors with a long-term interest in creating products for OpenMP, while auxiliary members have an interest in OpenMP and do not create or sell products.
9th International Workshop on OpenMP — September 16-18, 2013
The »International Workshop on OpenMP (IWOMP) is an annual workshop dedicated to the promotion and advancement of all aspects of parallel programming with OpenMP. It is the premier forum to present and discuss issues, trends, recent research ideas and results related to parallel programming with OpenMP. The international workshop affords an opportunity for OpenMP users as well as developers to come together for discussions and sharing new ideas and information on this topic.
IWOMP 2013 will be a three-day event. The first day will consist of tutorials focusing on topics of interest to current and prospective OpenMP developers, suitable for both beginners as well as those interested in learning of recent developments in the evolving OpenMP standard. The second and third days will consist of technical papers and panel sessions during which research ideas and results will be presented and discussed.
Go to »iwomp.org for more information.
The Clang community has announced the availability of a full OpenMP 3.1 support implementation in the Clang compiler:
The project is hosted here: http://clang-omp.github.com/
“It is based on clang 3.3 (and will be updated as new clang/llvm releases become available); also, we plan to eventually contribute everything to the clang trunk (initial patches have already been committed). This implementation supports 3.1 version of OpenMP standard in full; it passes all OpenMP tests we tried with it so far (this includes OpenMP Validation Suite from OpenUH Research Compiler, SPEC OMP2012 and internal Intel test suites). Performance-wise, it demonstrates similar gains and scalability as other compilers with OpenMP support.“
26 vendors and research organizations collaborating on standard parallel programming model
Red Hat has joined the OpenMP Architecture Review Board (ARB), a group of leading hardware and software vendors and research organizations creating the standard for today’s most prevalent shared-memory parallel programming model.
“As a leading proponent of OpenMP, Red Hat is thrilled to officially join the OpenMP ARB,” said Mike Werner, senior director, ISV and developer ecosystems, Red Hat. “We believe that the OpenMP model is important in simplifying parallel processing efforts, and implement the standard in Red Hat Enterprise Linux, including Red Hat Enterprise Linux 6.”
Red Hat is the world’s leading provider of open source solutions, using a community-powered approach to reliable and high-performing cloud, Linux, middleware, storage and virtualization technologies. The company’s membership in the OpenMP ARB exemplifies a long-standing commitment to the development and support of tools and standards that help advance open source technology and its adoption within the developer community.
“I am very pleased to have Red Hat participate in the OpenMP family”, said Michael Wong, OpenMP CEO. “Red Hat has a long tradition of supporting OpenMP with rapid GCC implementations and strong commitment to open standards. This will help to ensure deeper collaboration.”
The OpenMP ARB now has 14 permanent members and 12 auxiliary members. Permanent members, including Red Hat, are vendors who have a long-term interest in creating products for OpenMP, while auxiliary members are organizations with an interest in the standard but that do not create or sell OpenMP products.
The OpenMP 4.0 API Specification is released with Significant New Standard Features
The OpenMP 4.0 API supports the programming of accelerators, SIMD programming, and better optimization using thread affinity
The OpenMP Consortium has released OpenMP API 4.0, a major upgrade of the OpenMP API standard language specifications. Besides several major enhancements, this release provides a new mechanism to describe regions of code where data and/or computation should be moved to another computing device.
Bronis R. de Supinski, Chair of the OpenMP Language Committee, stated that “OpenMP 4.0 API is a major advance that adds two new forms of parallelism in the form of device constructs and SIMD constructs. It also includes several significant extensions for the loop-based and task-based forms of parallelism already supported in the OpenMP 3.1 API.”
The 4.0 specification is now available on the »OpenMP Specifications page.
Standard for parallel programming extends its reach
With this release, the OpenMP API specifications, the de-facto standard for parallel programming on shared memory systems, continues to extend its reach beyond pure HPC to include DSPs, real time systems, and accelerators. The OpenMP API aims to provide high-level parallel language support for a wide range of applications, from automotive and aeronautics to biotech, automation, robotics and financial analysis.
New features in the OpenMP 4.0 API include:
· Support for accelerators. The OpenMP 4.0 API specification effort included significant participation by all the major vendors in order to support a wide variety of compute devices. OpenMP API provides mechanisms to describe regions of code where data and/or computation should be moved to another computing device. Several prototypes for the accelerator proposal have already been implemented.
· SIMD constructs to vectorize both serial as well as parallelized loops. With the advent of SIMD units in all major processor chips, portable support for accessing them is essential. OpenMP 4.0 API provides mechanisms to describe when multiple iterations of the loop can be executed concurrently using SIMD instructions and to describe how to create versions of functions that can be invoked across SIMD lanes.
· Error handling. OpenMP 4.0 API defines error handling capabilities to improve the resiliency and stability of OpenMP applications in the presence of system-level, runtime-level, and user-defined errors. Features to abort parallel OpenMP execution cleanly have been defined, based on conditional cancellation and user-defined cancellation points.
· Thread affinity. OpenMP 4.0 API provides mechanisms to define where to execute OpenMP threads. Platform-specific data and algorithm-specific properties are separated, offering a deterministic behavior and simplicity in use. The advantages for the user are better locality, less false sharing and more memory bandwidth.
· Tasking extensions. OpenMP 4.0 API provides several extensions to its task-based parallelism support. Tasks can be grouped to support deep task synchronization and task groups can be aborted to reflect completion of cooperative tasking activities such as search. Task-to-task synchronization is now supported through the specification of task dependency.
· Support for Fortran 2003. The Fortran 2003 standard adds many modern computer language features. Having these features in the specification allows users to parallelize Fortran 2003 compliant programs. This includes interoperability of Fortran and C, which is one of the most popular features in Fortran 2003.
· User-defined reductions. Previously, OpenMP API only supported reductions with base language operators and intrinsic procedures. With OpenMP 4.0 API, user-defined reductions are now also supported.
· Sequentially consistent atomics. A clause has been added to allow a programmer to enforce sequential consistency when a specific storage location is accessed atomically.
This represents collaborative work by many of the brightest in industry, research, and academia, building on the consensus of 26 members. We strive to deliver high-level parallelism that is portable across 3 widely-implemented common General Purpose languages, productive for HPC and consumers, and delivers highly competitive performance. I want to congratulate all the members for coming together to create such a momentous advancement in parallel programming, under such tight constraints and industry challenges.
With this release, the OpenMP API will move immediately forward to the next release to bring even more usable parallelism to everyone. – Michael Wong, CEO OpenMP ARB.
- Neural Network Implementation Using CUDA and OpenMP
- New video on OpenMP: Dr Clay Breashears, Intel
- OpenMP Programming on Intel R Xeon Phi TM Coprocessors: An Early Performance Comparison
Tim Cramer, Dirk Schmidl, Michael Klemmy, Dieter an Mey
Recent publications of interest regarding OpenMP on the web:
- new release of GraphicsMagick Image Processing System
- Portuguese tutorial about OpenMP from Paulo Penteado, Post doctoral researcher at Departamento de Astronomia, Instituto de Astronomia, Geofísica e Ciências Atmosféricas, Universidade de São Paulo (IAG/USP).
- Debbie Greenstreet blogs about OpenMP and multicore systems on Texas Instruments Engineer to Engineer Multicore Mix.
24 vendors and research organizations now collaborating on developing shared-memory parallel programming model
Champaign, Illinois — May 2, 2013 — The Barcelona Supercomputing Center (BSC) has 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.
“We are proud to share our 15 years’ experience developing support for parallel programming models within the OpenMP community.”, says Mateo Valero, director of BSC, “Our researchers have been involved in OpenMP since the beginning, through cOMPunity. BSC has participated in the definition of the tasking model, lately with the inclusion of task dependences.”
“I look forward to BSC continuing their excellent technical contribution from the past into the future.”, says Michael Wong, OpenMP CEO.
Barcelona Supercomputing Center is an HPC research center that holds a significant group of Computer Science researchers and closely collaborates with IT Industry. Its Computer Science research covers all levels from the computer architecture to the parallel applications.
The OpenMP Architecture Review Board (ARB) now has 13 permanent members and 11 auxiliary members. Permanent members are vendors creating products for OpenMP. These are AMD, CAPS-Enterprise, Convey Computer, Cray, Fujitsu, HP, IBM, Intel, NEC, NVIDIA, Oracle Corporation, The Portland Group, Inc., and Texas Instruments. Auxiliary members are organizations with an interest in the standard but that do not sell OpenMP products. They are ANL, ASC/LLNL, BSC, cOMPunity, EPCC, LANL, NASA, ORNL, RWTH Aachen University, Sandia National Lab and the Texas Advanced Computing Center.