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We will now review a concrete example a le matcher that reads all the lenames given on the command line (including those in the directories given on the command line, recursively), and that outputs the absolute paths of those les that meet certain criteria We will start by looking at how pipelines are composed, and then we will look at the coroutines that provide the pipeline components Here is the simplest pipeline:

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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pipeline = get_files(receiver)

os walk()

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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

turn those requirements into a usable database We attack this topic in two phases: requirements gathering and requirements interpretation In this part, we talk through the requirements of Mountain View Music and describe how we went about extracting them

This pipeline prints every le it is given (or all the les in the directory it is given, recursively) The get_files() function is a coroutine that yields the lenames and the receiver is a reporter() coroutine created by receiver = reporter() that simply prints each lename it receives This pipeline does little more than the oswalk() function (and in fact uses that function), but we can use its components to compose more sophisticated pipelines

pipeline = get_files(suffix_matcher(receiver, ("htm", "html")))

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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