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Hands-On Data Science with Anaconda

You're reading from  Hands-On Data Science with Anaconda

Product type Book
Published in May 2018
Publisher Packt
ISBN-13 9781788831192
Pages 364 pages
Edition 1st Edition
Languages
Authors (2):
Yuxing Yan Yuxing Yan
Profile icon Yuxing Yan
James Yan James Yan
Profile icon James Yan
View More author details

Table of Contents (15) Chapters

Preface 1. Ecosystem of Anaconda 2. Anaconda Installation 3. Data Basics 4. Data Visualization 5. Statistical Modeling in Anaconda 6. Managing Packages 7. Optimization in Anaconda 8. Unsupervised Learning in Anaconda 9. Supervised Learning in Anaconda 10. Predictive Data Analytics – Modeling and Validation 11. Anaconda Cloud 12. Distributed Computing, Parallel Computing, and HPCC 13. References 14. Other Books You May Enjoy

Understanding MPI

Usually, a parallel algorithm needs to move data between different engines. One way to do so is by doing a pull and then a push using the direct view. However, this method is quite slow since all the data has to go through the controller to the client and then back through the controller, to its final destination. A much better way of moving data between engines is to use a message passing library, such as the Message Passing Interface (MPI). IPython's parallel computing architecture has been designed to integrate with MPI. To download and install Windows MPI, readers can refer to https://msdn.microsoft.com/en-us/library/bb524831%28v=vs.85%29.aspx.

In addition, you could install the mpi4py package.

R package Rmpi

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