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The Loop Parallelism pattern is heavily used by OpenMP programmers Annual workshops are held in North America (Wompat: Workshop on OpenMP Applications and Tools), Europe (EWOMP: European Workshop on OpenMP), and Japan (WOMPEI: Workshop on OpenMP Experiences and Implementations) to discuss OpenMP and its use Proceedings from many of these workshops are widely available [VJKT00, Sci03, EV01] and are full of examples of the Loop Parallelism pattern Most of the work on OpenMP has been restricted to shared memory multiprocessor machines for problems that work well with a nearly flat memory hierarchy Work has been done to extend OpenMP applications to more complicated memory hierarchies, including NUMA machines [NA01, SSGF00] and even clusters [HLCZ99, SHTS01] Related Patterns The concept of driving parallelism from a collection of loops is general and used with many patterns In particular, many problems using the SPMD pattern are loop based They use the UE ID, however, to drive the parallelization of the loop and hence don't perfectly map onto this pattern Furthermore, problems using the SPMD pattern usually include some degree of parallel logic in between the loops This allows them to decrease their serial fraction and is one of the reasons why SPMD programs tend to scale better than programs using the Loop Parallelism pattern Algorithms targeted for shared memory computers that use the Task Parallelism or Geometric Decomposition patterns frequently use the Loop Parallelism pattern
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57 THE FORK/JOIN PATTERN
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Problem In some programs, the number of concurrent tasks varies as the program executes, and the way these tasks are related prevents the use of simple control structures such as parallel loops How can a parallel program be constructed around such complicated sets of dynamic tasks Context In some problems, the algorithm imposes a general and dynamic parallel control structure Tasks are created dynamically (that is, forked) and later terminated (that is, joined with the forking task) as the
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program continues to execute In most cases, the relationships between tasks are simple, and dynamic task creation can be handled with parallel loops (as described in the Loop Parallelism pattern) or through task queues (as described in the Master/Worker pattern) In other cases, relationships between the tasks within the algorithm must be captured in the way the tasks are managed Examples include recursively generated task structures, highly irregular sets of connected tasks, and problems where different functions are mapped onto different concurrent tasks In each of these examples, tasks are forked and later joined with the parent task (that is, the task that executed the fork) and the other tasks created by the same fork These problems are addressed in the Fork/Join pattern As an example, consider an algorithm designed using the Divide and Conquer pattern As the program execution proceeds, the problem is split into subproblems and new tasks are recursively created (or forked) to concurrently execute subproblems; each of these tasks may in turn be further split When all the tasks created to handle a particular split have terminated and joined with the parent task, the parent task continues the computation This pattern is particularly relevant for Java programs running on shared memory computers and for problems using the Divide and Conquer and Recursive Data patterns OpenMP can be used effectively with this pattern when the OpenMP environment supports nested parallel regions Forces
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Algorithms imply relationships between tasks In some problems, there are complex or recursive relations between tasks, and these relations need to be created and terminated dynamically Although these can be mapped onto familiar control structures, the design in many cases is much easier to understand if the structure of the tasks is mimicked by the structure of the UEs A one to one mapping of tasks onto UEs is natural in these algorithms, but that must be balanced against the number of UEs a system can handle UE creation and destruction are costly operations The algorithm might need to be recast to decrease these operations so they don't adversely affect the program's overall performance
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Solution In problems that use the Fork/Join pattern, tasks map onto UEs in different ways We will discuss two different approaches to the solution: (1) a simple direct mapping where there is one task per UE, and (2) an indirect mapping where a pool of UEs work on sets of tasks
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