Undergraduate Topics in Computer Science: Introduction to HPC with MPI for Data Science

Frank Nielsen

ISBN 10: 3319219022 / ISBN 13: 9783319219028

Published by Springer, 2016

Leichte Kratzer / Abnutzungen / Druckstellen. This gentle introduction to High Performance Computing (HPC) for Data Science using the Message Passing Interface (MPI) standard serves as a foundational course for undergraduates on parallel programming within distributed memory models, requiring only basic programming knowledge. The book is divided into two parts. The first part focuses on high performance computing using C++ and MPI, covering essential concepts such as blocking versus non-blocking communications, global communications (e.g., broadcast, scatter), and collaborative computations (reduce). It also discusses Amdahl's and Gustafson's speed-up laws, parallel sorting, and linear algebra on clusters. Various cluster topologies, including ring, torus, and hypercube, are explained, along with global communication procedures. The section concludes with the MapReduce model, ideal for big data processing within the MPI framework. The second part shifts to high-performance data analytics, introducing flat and hierarchical clustering algorithms for data exploration, programming these algorithms on clusters, machine learning classification, and an introduction to graph analytics. It wraps up with a brief overview of data core-sets, making big data problems manageable. Each chapter includes exercises for practice, and a final exam helps students assess their understanding of the material. Seller Inventory # b47baf2b-d8e2-40bb-80b2-29063176d16f

Synopsis:

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.

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 Details

Title: Undergraduate Topics in Computer Science: ...
Publisher: Springer
Publication Date: 2016
Binding: Softcover
Condition: As New