HIGH PERFORMANCE COMPUTING
”Applications that use OpenMP are countless and in scientific and non-scientific domains”
g
the OpenMP API, published separate standards for Fortran and C, which were driven by Tim Mattson and Larry Meadows, respectively. In 2003, Mark Bull, from the Edinburgh Parallel Computing Centre (EPCC), became the new chair of the language committee and began an initiative to merge the Fortran and C specifications into a single, joint document. With the rise of HPC as a major tool, application algorithms became more complex and moved away from simple loop-based execution. Recognising this, the OpenMP API added support for task- based programming to its core language. This made it possible to parallelise irregular applications that use recursive algorithms or traverse non- array data structures such as graphs. A few years later, commodity
hardware began to move in a new direction, adopting the vector-like features of 1970s supercomputers by introduction single-instruction multiple-data (SIMD) processing. This shift required the OpenMP language to provide features that account for this new form of parallelism. The OpenMP API was extended to offer new constructs to let the compiler use SIMD instructions from these new processors. Starting around 2009,
HPC systems began to use specialised processors called accelerators or coprocessors. Graphics Processing Units (GPUs) were used to leverage their compute power in addition to traditional processors to accelerate the computations. Digital Signal Processors (DSPs) were used in embedded systems to
account for the specialised functions needed in signal processing and filtering. Programming support
for these new kinds of accelerators required new features to be added to the OpenMP API in order to meet the requirements of this hardware and programming environment. Bronis de Supinski, from Lawrence Livermore Lab, then leader of the language committee, started an accelerator subgroup in the OpenMP ARB, which designed new language features (device constructs) for offloading calculations and data to accelerators. The OpenMP API version 4.0 was released with device constructs in July 2013. About this time, Texas Instruments released an OpenMP toolkit for its DSPs and its SoCs, and Intel released its coprocessor based on the Many-Integrated Core (MIC) architecture with OpenMP support. With these releases and other support in the ecosystem, the extension of the OpenMP API from pure shared-memory systems to accelerators and embedded systems was a reality. Since then multiple vendors of accelerator and embedded systems have released products based on the OpenMP API for offloading data and calculations. Applications that use the OpenMP programming model for parallelisation are countless and can be found in many scientific and non-scientific domains. The list includes, and is certainly not limited to, commercial applications such as the Altair RADIOSS OptiStruct and FEKO products, and the DATADVANCE MACROS product; and open- source applications such as NWChem for quantum chemistry, Amber for molecular dynamics, WRF for weather forecasting, eFindSite for bioinformatics, and StingRay for radio frequency ray tracing.
Members
The OpenMP ARB is the body that oversees the
18 Scientific Computing World April/May 2017
OpenMP language and promotes it world-wide. Since its inauguration, the OpenMP ARB has been a vendor-neutral organisation (formally a US non-profit), with many different hardware and software vendors, academics and important HPC sites participating in defining the OpenMP API. A full list of current members can be found on the OpenMP website (
http://openmp.org; the source for all things related to OpenMP). The number of members has increased from the original eight to 29 members, and the list is growing.
The ARB membership has
prominent representation, with members including AMD, ARM, Cavium, Cray, Fujitsu, IBM, Intel, Micron, NEC, NVIDIA,
”For user queries and discussions, there is an active OpenMP group on StackOverflow”
Oracle, Red Hat, and Texas Instruments. Given that list, there are only two machines in the November 2017 Top500 list which use processors from a vendor who is not an OpenMP ARB member! Similarly, major HPC sites such as the US national labs, Barcelona Supercomputing Center, INRIA and EPCC are involved along with leading technical universities. Any organisation interested in helping to drive the future of the OpenMP API, or wanting to know what will be in the next version of the standard as it is being worked on, is invited to join the OpenMP ARB.
Workshops Over the years, several conferences and workshops have been established to provide the community a forum for knowledge exchange. The main research
workshop is the yearly
International Workshop on OpenMP (IWOMP) that emerged from the EWOMP, WOMPAT, and WOMPEI workshops. IWOMP alternates between the US, Europe, and the Asia Pacific region. Workshop proceedings appear in the renowned Springer Lecture Notes on Computer Science and attract many researchers focusing on OpenMP development. Of course, other HPC- themed conferences also welcome research based on the OpenMP programming language. OpenMPCon is a newer
conference which gathers the OpenMP community to present their use of the OpenMP API in their applications and to provide a platform for knowledge exchange between OpenMP developers and the OpenMP ARB. OpenMPCon is co- located with IWOMP. The inaugural UK OpenMP
Users’ Conference will be in Oxford in May. Similar to OpenMPCon, this conference is intended to be a forum for OpenMP users to meet, discuss their achievements and findings, and to engage with the OpenMP ARB. For user queries and
discussions, there is an active OpenMP group on StackOverflow, as well as support through a forum on the OpenMP website (http://
forum.openmp.org/forum/). There are also many OpenMP courses.
The Next 20 Years Over the last 20 years, the OpenMP language has demonstrated it can adapt to changes in the hardware and software environment, while maintaining its utmost relevance in HPC. Since the beginning it has been a key programming model that enables the use of modern hardware, without requiring the use of vendor- specific languages and its many supporters intend to ensure that remains true for the next 20 years.
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