Introduction to HPC with Mpi for Data Science
Nielsen, Frank
ISBN 10: 3319219022 ISBN 13: 9783319219028
Published by Springer, 2016
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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.
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Title: Introduction to HPC with Mpi for Data Science
Publisher: Springer
Publication Date: 2016
Binding: Paperback
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Taschenbuch. Condition: Neu. Introduction to HPC with MPI for Data Science | Frank Nielsen | Taschenbuch | xxxiii | Englisch | 2016 | Springer | EAN 9783319219028 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Seller Inventory # 104571910
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Introduction to HPC with MPI for Data Science
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Seller Inventory # 9783319219028
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Introduction to HPC with MPI for Data Science
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Taschenbuch. Condition: Neu. Neuware -This gentle introduction to High Performance Computing (HPC) for DataScience using the Message Passing Interface (MPI) standard has beendesigned as a first course for undergraduates on parallel programming ondistributed memory models, and requires only basic programming notions.Dividedinto two parts the first part covers high performance computing usingC++ with the Message Passing Interface (MPI) standard followed by asecond part providing high-performance data analytics on computerclusters.In the first part, the fundamental notions of blockingversus 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 addressingparallel sorting and parallel linear algebra on computer clusters. Thecommon ring, torus and hypercube topologies of clusters are thenexplained and global communication procedures on these topologies arestudied. This first part closes with the MapReduce (MR) model ofcomputation well-suited to processing big data using the MPI framework.Inthe second part, the book focuses on high-performance data analytics.Flat and hierarchical clustering algorithms are introduced for dataexploration along with how to program these algorithms on computerclusters, followed by machine learning classification, and anintroduction to graph analytics. This part closes with a conciseintroduction to data core-sets that let big data problems be amenable totiny data problems.Exercises are included at the end of eachchapter in order for students to practice the concepts learned, and afinal section contains an overall exam which allows them to evaluate howwell they have assimilated the material covered in the book.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 316 pp. Englisch. Seller Inventory # 9783319219028
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Introduction to HPC with MPI for Data Science
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 316 pp. Englisch. Seller Inventory # 9783319219028
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