Titlebook: Automatic Parallelization; An Overview of Funda Samuel P. Midkiff Book 2012 Springer Nature Switzerland AG 2012

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978-3-031-00608-1Springer Nature Switzerland AG 2012

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https://doi.org/10.1007/978-94-007-4110-2ould prevent any parallelization of the loop. In practice, it is often necessary to perform additional transformations to allow a loop to be partially or completely parallelized. Transformations to eliminate dependences, or to allow them to be ignored, are discussed in Chapter 4. Other transformatio

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Climate Change, Migration, and Development,e and parallelization, from being performed. In this chapter, several transformations that modify, or completely eliminate dependences will be described. These transformations typically do not lead to an increase in performance—on the contrary, they may lead to more loop bounds and subscript index e

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Global Perspectives on Orhan Pamukre processors. Nevertheless, all large machines for high-performance numerical computing, as well as many small scale clusters, have a physically distributed memory architecture and are programmed using a distributed memory programming model.

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Introduction and overview,cessors [106, 110], the field has a long history, and predates the advent of the multicore processor by decades. Research has focused on several goals, the most ambitious being support for automatic parallelization. The goal of automatic parallelization is for a compiler to take a “dusty deck" (an u

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