Parallel Computing for Data Science w/ Examples in R, C++, CUBA

Matloff,Norman

ISBN 10: 0367738198 / ISBN 13: 9780367738198

Verlag: CRC Press, 2020

Beschreibung

".a thorough, but readable guide to parallel computing?one that can be used by researchers and students in a wide range of disciplines. In my view, this book will meet that need. ? For me and colleagues in my field, I would see this as a ?must-have? reference book." David E. Giles, University of Victoria. "This is a book that I will use, both as a reference and for instruction. The examples are poignant and the presentation moves the reader directly from concept to working code." Michael Kane, Yale University. Parallel Computing for Data Science: With Examples in R, C++ and CUDA is one of the first parallel computing books to concentrate exclusively on parallel data structures, algorithms, software tools, and applications in data science. It includes examples not only from the classic "n observations, p variables" matrix format but also from time series, network graph models, and numerous other structures common in data science. The book also discusses software packages that span more than one type of hardware and can be used from more than one type of programming language. Dr. Norman Matloff is a professor of computer science at the University of California, Davis, where he was a founding member of the Department of Statistics. He is a statistical consultant and a former database software developer. He earned a PhD in pure mathematics from UCLA. 480 gr.

Bestandsnummer des Verkäufers 9780367738198LEA88973

Bibliografische Details

• Titel: Parallel Computing for Data Science w/ Examples in R, C++, CUBA
• Verlag: CRC Press
• Veröffentlichungsdatum: 2020
• Sprache: Englisch
• ISBN-10: 0367738198
• ISBN-13: 9780367738198
• Bindung: Blanda
• Illustrator: No Aplica
• Ausgabe: 1.
• Zustand: New
• Zustand des Schutzumschlags: Nuevo
• Buchtyp: Libro

Über diesen Titel

Inhaltsangabe

Parallel Computing for Data Science: With Examples in R, C++ and CUDA is one of the first parallel computing books to concentrate exclusively on parallel data structures, algorithms, software tools, and applications in data science. It includes examples not only from the classic "n observations, p variables" matrix format but also from time series, network graph models, and numerous other structures common in data science. The examples illustrate the range of issues encountered in parallel programming.

With the main focus on computation, the book shows how to compute on three types of platforms: multicore systems, clusters, and graphics processing units (GPUs). It also discusses software packages that span more than one type of hardware and can be used from more than one type of programming language. Readers will find that the foundation established in this book will generalize well to other languages, such as Python and Julia.

Über die Autorin bzw. den Autor

Dr. Norman Matloff is a professor of computer science at the University of California, Davis, where he was a founding member of the Department of Statistics. He is a statistical consultant and a former database software developer. He has published numerous articles in prestigious journals, such as the ACM Transactions on Database Systems, ACM Transactions on Modeling and Computer Simulation, Annals of Probability, Biometrika, Communications of the ACM, and IEEE Transactions on Data Engineering. He earned a PhD in pure mathematics from UCLA, specializing in probability/functional analysis and statistics.