Overview
- Contains numerous exercises and a test exam
- Features material that has been used and tested with students
- Provides additional material, including source C++/MPI codes and slides for each chapter, on an accompanying website
- Includes supplementary material: sn.pub/extras
Part of the book series: Undergraduate Topics in Computer Science (UTICS)
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About this book
This gentle introduction to High Performance Computing (HPC) for Data Science using the Message Passing Interface (MPI) standard has been designed as a first course for undergraduates on parallel programming on distributed memory models, and requires only basic programming notions.
Divided into two parts the first part covers high performance computing using C++ with the Message Passing Interface (MPI) standard followed by a second part providing high-performance data analytics on computer clusters.
In the first part, the fundamental notions of blocking versus non-blocking point-to-point communications, global communications (like broadcast or scatter) and collaborative computations (reduce), with Amdalh and Gustafson speed-up laws are described before addressing parallel sorting and parallel linear algebra on computer clusters. The common ring, torus and hypercube topologies of clusters are then explained and global communication procedures on these topologies are studied. This first part closes with the MapReduce (MR) model of computation well-suited to processing big data using the MPI framework.
In the second part, the book focuses on high-performance data analytics. Flat and hierarchical clustering algorithms are introduced for data exploration along with how to program these algorithms on computer clusters, followed by machine learning classification, and an introduction to graph analytics. This part closes with a concise introduction to data core-sets that let big data problems be amenable to tiny data problems.
Exercises are included at the end of each chapter in order for students to practice the concepts learned, and a final section contains an overall exam which allows them to evaluate how well they have assimilated the material covered in the book.
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Keywords
Table of contents (11 chapters)
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Front Matter
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High Performance Computing (HPC) with the Message Passing Interface (MPI)
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Front Matter
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High Performance Computing (HPC) for Data Science (DS)
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Front Matter
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Back Matter
Authors and Affiliations
About the author
Frank Nielsen is a Professor at École Polytechnique in France where he teaches graduate (vision/graphics) and undergraduate (Java/algorithms),and a senior researcher at Sony Computer Science Laboratories Inc. His research includes Computational information geometry for imaging and learning and he is the author of 3 textbooks and 3 edited books. He is also on the Editorial Board for the Springer Journal of Mathematical Imaging and Vision.
Bibliographic Information
Book Title: Introduction to HPC with MPI for Data Science
Authors: Frank Nielsen
Series Title: Undergraduate Topics in Computer Science
DOI: https://doi.org/10.1007/978-3-319-21903-5
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing Switzerland 2016
Softcover ISBN: 978-3-319-21902-8Published: 11 February 2016
eBook ISBN: 978-3-319-21903-5Published: 03 February 2016
Series ISSN: 1863-7310
Series E-ISSN: 2197-1781
Edition Number: 1
Number of Pages: XXXIII, 282
Number of Illustrations: 101 illustrations in colour
Topics: Programming Techniques