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The simplest case is one where we map each sub task to a UE As new subtasks are forked, new UEs are created to handle them This will build up corresponding sets of tasks and UEs In many cases, there is a synchronization point where the main task waits for its subtasks to finish This is called a join After a subtask terminates, the UE handling it will be destroyed We will provide an example of this approach later using Java The direct task/UE mapping solution to the Fork/Join pattern is the standard programming model in

We have used the same @coroutine decorator that we created in the previous subsubsection The get_files() coroutine is essentially a wrapper around the oswalk() function and that expects to be given paths or lenames to work on

Once you have the first cut of the requirements, you start turning them into a data model In 6, Interpreting Requirements, we look at how you take the requirements, which are in human language, and turn them into a data model We look not only at extracting the information required for the model, but also at extracting business rules

@coroutine def get_files(receiver): while True: path = (yield) if ospathisfile(path): receiversend(ospathabspath(path)) else: for root, dirs, files in oswalk(path): for filename in files: receiversend(ospathabspath( ospathjoin(root, filename)))

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OpenMP A program begins as a single thread (the master thread) A parallel construct forks a team of threads, the threads execute within a shared address space, and at the end of the parallel construct, the threads join back together The original master thread then continues execution until the end of the program or until the next parallel construct[3] This structure underlies the implementation of the OpenMP parallel loop constructs described in the Loop Parallelism pattern

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This coroutine has the now-familiar structure: an in nite loop in which we wait for the yield to return a value that we can process, and then we send the result to the receiver

mapping solution This approach has been successfully used in [AML 99 + ] The OpenMP specification, however, lets conforming OpenMP implementations "serialize" nested parallel regions (that is, execute them with a team of size one) Therefore, an OpenMP program cannot depend on nested parallel regions actually forking additional threads, and programmers must be cautious when using OpenMP for all but the simplest fork/join programs

@coroutine def suffix_matcher(receiver, suffixes): while True: filename = (yield) if filenameendswith(suffixes): receiversend(filename)

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This coroutine looks simple and it is but notice that it sends only lenames that match the suf xes, so any that don t match are ltered out of the pipeline

@coroutine def size_matcher(receiver, minimum=None, maximum=None): while True: filename = (yield) size = ospathgetsize(filename) if ((minimum is None or size >= minimum) and (maximum is None or size <= maximum)): receiversend(filename)

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This coroutine is almost identical to suffix_matcher(), except that it lters out les whose size is not in the required range, rather than those which don t have a matching suf x The pipeline we have created suffers from a couple of problems One problem is that we never close any of the coroutines In this case it doesn t matter, since the program terminates once the processing is nished, but it is probably better to get into the habit of closing coroutines when we are nished with them Another problem is that potentially we could be asking the operating system (under the hood) for different pieces of information about the same le in several parts of the pipeline and this could be slow A solution is to modify the get_files() coroutine so that it returns (filename, osstat()) 2-tuples for each le rather than just lenames, and then pass these 2-tuples through the pipeline This would mean that we acquire all the relevant information just once per le You ll get the chance to solve both of these problems, and to add additional functionality, in an exercise at the end of the chapter Creating coroutines for use in pipelines requires a certain reorientation of thinking However, it can pay off handsomely in terms of exibility, and for large data sets can help minimize the amount of data held in memory as well as potentially resulting in faster throughput

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