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The key to parallel computing is exploitable concurrency Concurrency exists in a computational problem when the problem can be decomposed into subproblems that can safely execute at the same time To be of any use, however, it must be possible to structure the code to expose and later exploit the concurrency and permit the subproblems to actually run concurrently; that is, the concurrency must be exploitable Most large computational problems contain exploitable concurrency A programmer works with exploitable concurrency by creating a parallel algorithm and implementing the algorithm using a parallel programming environment When the resulting parallel program is run on a system with multiple processors, the amount of time we have to wait for the results of the computation is reduced In addition, multiple processors may allow larger problems to be solved than could be done on a single processor system As a simple example, suppose part of a computation involves computing the summation of a large set of values If multiple processors are available, instead of adding the values together sequentially, the set can be partitioned and the summations of the subsets computed simultaneously, each on a different processor The partial sums are then combined to get the final answer Thus, using multiple processors to compute in parallel may allow us to obtain a solution sooner Also, if each processor has its own memory, partitioning the data between the processors may allow larger problems to be handled than could be handled on a single processor This simple example shows the essence of parallel computing The goal is to use multiple processors to solve problems in less time and/or to solve bigger problems than would be possible on a single processor The programmer's task is to identify the concurrency in the problem, structure the algorithm so that this concurrency can be exploited, and then implement the solution using a suitable programming environment The final step is to solve the problem by executing the code on a parallel system Parallel programming presents unique challenges Often, the concurrent tasks making up the problem include dependencies that must be identified and correctly managed The order in which the tasks execute may change the answers of the computations in nondeterministic ways For example, in the parallel summation described earlier, a partial sum cannot be combined with others until its own computation has completed The algorithm imposes a partial order on the tasks (that is, they must complete before the sums can be combined) More subtly, the numerical value of the summations may change slightly depending on the order of the operations within the sums because floating point
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arithmetic is nonassociative A good parallel programmer must take care to ensure that nondeterministic issues such as these do not affect the quality of the final answer Creating safe parallel programs can take considerable effort from the programmer Even when a parallel program is "correct", it may fail to deliver the anticipated performance improvement from exploiting concurrency Care must be taken to ensure that the overhead incurred by managing the concurrency does not overwhelm the program runtime Also, partitioning the work among the processors in a balanced way is often not as easy as the summation example suggests The effectiveness of a parallel algorithm depends on how well it maps onto the underlying parallel computer, so a parallel algorithm could be very effective on one parallel architecture and a disaster on another We will revisit these issues and provide a more quantitative view of parallel computation in the next chapter
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A design pattern describes a good solution to a recurring problem in a particular context The pattern follows a prescribed format that includes the pattern name, a description of the context, the forces (goals and constraints), and the solution The idea is to record the experience of experts in a way that can be used by others facing a similar problem In addition to the solution itself, the name of the pattern is important and can form the basis for a domain specific vocabulary that can significantly enhance communication between designers in the same area Design patterns were first proposed by Christopher Alexander The domain was city planning and architecture [AIS77] Design patterns were originally introduced to the software engineering community by Beck and Cunningham [BC87] and became prominent in the area of object oriented programming with the publication of the book by Gamma, Helm, Johnson, and Vlissides [GHJV95], affectionately known as the GoF (Gang of Four) book This book gives a large collection of design patterns for object oriented programming To give one example, the Visitor pattern describes a way to structure classes so that the code implementing a heterogeneous data structure can be kept separate from the code to traverse it Thus, what happens in a traversal depends on both the type of each node and the class that implements the traversal This allows multiple functionality for data structure traversals, and significant flexibility as new functionality can be added without having to change the data structure class The patterns in the GoF book have entered the lexicon of object oriented programming references to its patterns are found in the academic literature, trade publications, and system documentation These patterns have by now become part of the expected knowledge of any competent software engineer An educational nonprofit organization called the Hillside Group [Hil] was formed in 1993 to promote the use of patterns and pattern languages and, more generally, to improve human communication about computers "by encouraging people to codify common programming and design practice" To develop new patterns and help pattern writers hone their skills, the Hillside Group sponsors an annual Pattern Languages of Programs (PLoP) workshop and several spinoffs in other parts of the world, such as ChiliPLoP (in the western United States), KoalaPLoP (Australia), EuroPLoP (Europe), and
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Mensore PLoP (Japan) The proceedings of these workshops [Pat] provide a rich source of patterns covering a vast range of application domains in software development and have been used as a basis for several books [CS95, VCK96, MRB97, HFR99] In his original work on patterns, Alexander provided not only a catalog of patterns, but also a pattern language that introduced a new approach to design In a pattern language, the patterns are organized into a structure that leads the user through the collection of patterns in such a way that complex systems can be designed using the patterns At each decision point, the designer selects an appropriate pattern Each pattern leads to other patterns, resulting in a final design in terms of a web of patterns Thus, a pattern language embodies a design methodology and provides domain specific advice to the application designer (In spite of the overlapping terminology, a pattern language is not a programming language)
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