An Introduction to Parallel Programming by Peter Pacheco

By Peter Pacheco

Writer Peter Pacheco makes use of an educational method of express scholars easy methods to strengthen potent parallel courses with MPI, Pthreads, and OpenMP. the 1st undergraduate textual content to without delay deal with compiling and working parallel courses at the new multi-core and cluster structure, An creation to Parallel Programming explains tips to layout, debug, and evaluation the functionality of allotted and shared-memory courses. undemanding workouts train scholars how you can bring together, run and regulate instance programs.

Key features:

  • Takes an educational technique, beginning with small programming examples and development steadily to tougher examples
  • Focuses on designing, debugging and comparing the functionality of allotted and shared-memory programs
  • Explains tips to advance parallel courses utilizing MPI, Pthreads, and OpenMP programming models
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    Extra info for An Introduction to Parallel Programming

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    Framework problems and experiences. , Johnson, R. ) Building Application Frameworks: Object-Oriented Foundations of Frameworks Design. Wiley, New York (1999) 1 Metadata-Based Frameworks in the Context of Cloud Computing 23 13. : Design Patterns for Object-Oriented Software Development. Addison Wesley, Reading (1995) 14. : Evolution, architecture, and metamorphosis (Chap. 13). , Kerth, N. ) Pattern Languages of Program Design 2, pp. 295–314. AddisonWesley Longman, Boston (1996) 15. NET framework.

    Wiley, New York (1999) 1 Metadata-Based Frameworks in the Context of Cloud Computing 23 13. : Design Patterns for Object-Oriented Software Development. Addison Wesley, Reading (1995) 14. : Evolution, architecture, and metamorphosis (Chap. 13). , Kerth, N. ) Pattern Languages of Program Design 2, pp. 295–314. AddisonWesley Longman, Boston (1996) 15. NET framework. In: International Conference on Computer Systems and Technologies, 5, 2004, Rousse. pp. 1–6 (2004) 16. : Inference of reference immutability.

    We set up and configured Hadoop on several Amazon EC2 nodes and optimized to find the number and type of EC2 machines that would yield the best performance. We tested 20 large instances along with 4 Cluster Compute Instances. Because of the nature of our image processing and the nuances of the Hadoop framework, large amounts of binary data were transferred between the nodes in the cluster. Since the Cluster Compute Instances were tuned for network performance, that smaller cluster yielded better performance and was able to complete the image tiling process faster.

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