OpenMP also addresses the inability of previous shared-memory directive sets to deal with coarse grain parallelism. In the past, limited support for coarse grain work has led to developers thinking that shared-memory parallel programming was inherently limited to fine-grain parallelism -- this isn't the case with OpenMP. Orphaned directives in OpenMP offer the features necessary to represent coarse-grained algorithms.

HPF? HPF has never really gained wide acceptance among parallel application developers or hardware vendors. Some applications written in HPF perform well, but others find that limitations resulting from the HPF language itself or the compiler implementations lead to disappointing performance. HPF's focus on data parallelism has also limited its appeal.

Pthreads? Pthreads have never been targeted toward the technical/HPC market. This is reflected in the minimal Fortran support, and its lack of support for data parallelism. Even for C applications, pthreads requires programming at a level lower than most technical developers would prefer.

FORALL loops? FORALL loops are not rich or general enough to use as a complete parallel programming model. Their focus on loops and the rule that subroutines called by those loops can't have side effects effectively limit their scalability. FORALL loops are useful for providing information to automatic parallelizing compilers and preprocessors.

BSP or LINDA or SISAL or...? There are lots of parallel programming languages being researched or prototyped in the industry. These may be targeted towards a specific architecture, or focused on exploring one key requirement. If you have a question about how OpenMP compares with a specific language or model, we can help you figure this out.

OpenMP allows you to use loop-level parallelism as a way to start scaling your application for multiple processors, but then move into coarser grain parallelism, while maintaining the value of your earlier investment. This incremental development strategy avoids the all-or-none risks involved in moving to message-passing or other parallel programming models.

Orphaning allows parallel directives to be specified outside the lexical extent of parallel regions. A subroutine can be written for use from a number of parallel regions, and parallel directives needed by that subroutine embedded within its source, instead of having to be replicated everywhere that calls it. This is a natural place to speficy the parallelism, and avoids programming errors that result when the earlier style is used for complex applications. Orphaning is crucial to implementing coarse grain parallel algorithms, and to the development of portable, parallel libraries.

Systems that don't fit the classic shared-memory architecture may provide hardware or software layers that present the appearance of a shared-memory system, but often at the cost of higher latencies or special limitations. For example, OpenMP could be implemented for a distributed memory system on top of MPI, so OpenMP's latencies would be greater than that of MPI (whereas typically the reverse is the case on a shared-memory system). The extent to which these latencies or limitations reduce application portability or performance will help dictate whether vendors choose to develop OpenMP implementations for distributed memory systems.

Goals - The OpenMP definition was driven by a small number of experts working to an aggressive schedule, building primarily on current practice.

Technical - OpenMP includes better support for more scalable, coarse grain parallelism and more directives for managing private/shared data. OpenMP also includes query functions, environment variables, and conditional compilation support.

Timing - The need for a standard in this area is better accepted throughout the industry now, as vendors have begun to converge on system architectures that combine aspects of both shared-memory and distributed architectures of the past. The importance of a standard encouraging scalable, parallel application development that can exploit shared-memory hardware is recognized as being more important than ever. (Interest in PCF and X3H5 was partly derailed by the appearance of pure distributed memory MPP systems, whose proponents were arguing that shared-memory parallel programming was no longer interesting.)

Vendor support - The system vendors behind OpenMP collectively have delivered a very large share of the shared-memory parallel systems in use today.