An Introduction to Parallel Programming - download pdf or read online

By Peter Pacheco

Writer Peter Pacheco makes use of an instructional method of exhibit scholars find out how to increase potent parallel courses with MPI, Pthreads, and OpenMP. the 1st undergraduate textual content to without delay tackle compiling and operating parallel courses at the new multi-core and cluster structure, An advent to Parallel Programming explains the right way to layout, debug, and overview the functionality of allotted and shared-memory courses. undemanding workouts educate scholars how you can collect, run and adjust instance programs.

Key features:

  • Takes an academic process, beginning with small programming examples and construction steadily to tougher examples
  • Focuses on designing, debugging and comparing the functionality of dispensed and shared-memory programs
  • Explains the best way to boost parallel courses utilizing MPI, Pthreads, and OpenMP programming models
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    Additional resources for An Introduction to Parallel Programming

    Sample text

    To select datasets to use, several filters may be applied: data mining task, attribute type, data type, application area, size, etc. Let us remark that not many very large size datasets are available. com/datasets/ – provides links to 1) government and public data sites and portals, 2) data APIs from marketplaces, search engines, etc. and 3) data mining and data science competitions. com – is the leading platform for data prediction competitions and lists current data science competitions. es/keel/ – is an open source java software for data mining and provides a dataset repository with more than 900 datasets.

    To evaluate the quality of solutions, several indicators may be used depending on whether the optimal solution is known or not. If the optimal solution is known, then the difference in quality between the solution obtained and the known optimal solution can be computed. If the optimal solution is not known, then some lower/upper bounds may be used for computation. Otherwise, only comparative indicators between values obtained by the different methods may be used. Let us remark that the values compared are not systematically the values of the objective function, but may be the values of the optimization criterion identified for the problem.

    The neighborhood structure defined, thanks to the operator, plays a crucial role in the performance of metaheuristics and may lead to efficient, or inefficient, optimization methods. Hence the design of the neighborhood operator should be carried out with care. In addition to this neighborhood definition, the concept of local optimum must be defined. – Given a neighborhood operator N , a solution s ∈ D is a local optimum if none of its neighbors has a better quality. In a minimization context, this may be expressed by: s is a local optimum, iff ∀s ∈ N (s), f (s) ≤ f (s ) .

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