Parallel Computing for Data Science

With Examples in R, C++ and CUDA

Norman Matloff (Autor)

Buch | Hardcover

328 Seiten

2015
Crc Press Inc (Verlag)
978-1-4665-8701-4 (ISBN)

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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.

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.

Introduction to Parallel Processing in R. "Why Is My Program So Slow?": Obstacles to Speed. Principles of Parallel Loop Scheduling. The Shared Memory Paradigm: A Gentle Introduction through R. The Shared Memory Paradigm: C Level. The Shared Memory Paradigm: GPUs. Thrust and Rth. The Message Passing Paradigm. MapReduce Computation. Parallel Sorting and Merging. Parallel Prefix Scan. Parallel Matrix Operations. Inherently Statistical Approaches: Subset Methods. Appendices.

Reihe/Serie	Chapman & Hall/CRC The R Series
Zusatzinfo	4 Tables, black and white; 7 Illustrations, black and white
Verlagsort	Bosa Roca
Sprache	englisch
Maße	156 x 234 mm
Gewicht	662 g
Themenwelt	Mathematik / Informatik ► Informatik ► Software Entwicklung
	Mathematik / Informatik ► Informatik ► Theorie / Studium
	Informatik ► Weitere Themen ► Hardware
	Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra
	Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
ISBN-10	1-4665-8701-6 / 1466587016
ISBN-13	978-1-4665-8701-4 / 9781466587014
Zustand	Neuware