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Scientific Computing with Python

You're reading from   Scientific Computing with Python High-performance scientific computing with NumPy, SciPy, and pandas

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Product type Paperback
Published in Jul 2021
Publisher Packt
ISBN-13 9781838822323
Length 392 pages
Edition 2nd Edition
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Authors (4):
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Olivier Verdier Olivier Verdier
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Olivier Verdier
Jan Erik Solem Jan Erik Solem
Author Profile Icon Jan Erik Solem
Jan Erik Solem
Claus Führer Claus Führer
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Claus Führer
Claus Fuhrer Claus Fuhrer
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Claus Fuhrer
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Table of Contents (23) Chapters Close

Preface 1. Getting Started 2. Variables and Basic Types FREE CHAPTER 3. Container Types 4. Linear Algebra - Arrays 5. Advanced Array Concepts 6. Plotting 7. Functions 8. Classes 9. Iterating 10. Series and Dataframes - Working with Pandas 11. Communication by a Graphical User Interface 12. Error and Exception Handling 13. Namespaces, Scopes, and Modules 14. Input and Output 15. Testing 16. Symbolic Computations - SymPy 17. Interacting with the Operating System 18. Python for Parallel Computing 19. Comprehensive Examples 20. About Packt 21. Other Books You May Enjoy 22. References

18.3.5 One-to-all and all-to-one communication

When a complex task depending on a larger amount of data is divided into subtasks, the data also has to be divided into portions relevant to the related subtask and the results have to be assembled and processed into a final result.

Let's consider as an example the scalar product of two vectors  divided into subtasks:

                                  

with  All subtasks perform the same operations on portions of the initial data, the results have to be summed up, and possibly any remaining operations have to be carried out.

We have to perform the following steps:

  1. Creating the vectors u and v
  2. Dividing them into m subvectors with a balanced number of elements, that is,  elements if N is divisible by m, otherwise some subvectors have more elements
  3. Communicating each subvector to "its" processor
  4. Performing the scalar...
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