Parallel Computing for Data Science

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.

"From my reading of the book, Matloff achieves his goals, and in doing so he has provided a volume that will be immensely useful to a very wide audience. I can see it being used as a reference by data analysts, statisticians, engineers, econometricians, biometricians, etc. This would apply to both established researchers and graduate students. This book provides exactly the sort of information that this audience is looking for, and it is presented in a very accessible and friendly manner."—Econometrics Beat: Dave Giles’ Blog, July 2015"The author has correctly recognized that there is a pressing need for 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—one that would be well thumbed!"—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"Matloff’s Parallel Computing for Data Science: With Examples in R, C++ and CUDA can be recommended to colleagues and students alike, and the author is to be congratulated for taming a difficult and exhaustive body of topics via a very accessible primer."—Dirk Eddelbuettel, Debian and R Projects

• 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.