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Introduction to HPC with MPI for Data Science

Frank Nielsen

$96.95   $82.36

Paperback

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English
Springer International Publishing AG
11 February 2016
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.
By:  
Imprint:   Springer International Publishing AG
Country of Publication:   Switzerland
Edition:   1st ed. 2016
Dimensions:   Height: 235mm,  Width: 155mm,  Spine: 17mm
Weight:   4.803kg
ISBN:   9783319219028
ISBN 10:   3319219022
Series:   Undergraduate Topics in Computer Science
Pages:   282
Publication Date:  
Audience:   College/higher education ,  A / AS level
Format:   Paperback
Publisher's Status:   Active

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.

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