Chemical manufacturers and pharmaceutical companies, for example, could simulate the chain of chemical reactions needed to design new and far more complex compounds and materials.
OpenMP programs accomplish parallelism exclusively through the use of threads. A thread of execution is the smallest unit of processing that can be scheduled by an operating system.
The idea of a subroutine that can be scheduled to run autonomously might help explain what a thread is. Threads exist within the resources of a single process.
Without the process, they cease to exist.
However, the actual use of threads is up to the application. OpenMP is an explicit not automatic programming model, offering the programmer full control over parallelization. Parallelization can be as simple as taking a serial program and inserting compiler directives Or as complex as inserting subroutines to set multiple levels of parallelism, locks and even nested locks.
OpenMP is an Application Program Interface (API), jointly defined by a group of major computer hardware and software vendors. OpenMP provides a portable, scalable model for developers of shared memory parallel applications. The Career Cornerstone Center is an ever-expanding resource for anyone interested in exploring career opportunities in science, technology, engineering, mathematics, computing, and healthcare. The mission of the Ying Wu College of Computing, which was established in , is to bring education in a broad range of computing disciplines to students on campus and at a distance to carry out cutting-edge research while working closely in the industry.
Fork - Join Model: OpenMP uses the fork-join model of parallel execution: All OpenMP programs begin as a single process: The master thread executes sequentially until the first parallel region construct is encountered. The statements in the program that are enclosed by the parallel region construct are then executed in parallel among the various team threads.
When the team threads complete the statements in the parallel region construct, they synchronize and terminate, leaving only the master thread. The number of parallel regions and the threads that comprise them are arbitrary.
Because OpenMP is a shared memory programming model, most data within a parallel region is shared by default. All threads in a parallel region can access this shared data simultaneously. OpenMP provides a way for the programmer to explicitly specify how data is "scoped" if the default shared scoping is not desired.
This topic is covered in more detail in the Data Scope Attribute Clauses section. The API provides for the placement of parallel regions inside other parallel regions.
Implementations may or may not support this feature. The API provides for the runtime environment to dynamically alter the number of threads used to execute parallel regions.
Intended to promote more efficient use of resources, if possible. OpenMP provides a "relaxed-consistency" and "temporary" view of thread memory in their words. In other words, threads can "cache" their data and are not required to maintain exact consistency with real memory all of the time.
When it is critical that all threads view a shared variable identically, the programmer is responsible for insuring that the variable is FLUSHed by all threads as needed. More on this laterCloud Computing Key Initiative Overview Hype Cycle for Cloud Computing, ID: G Cloud computing is the most hyped subject in IT today.
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