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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.
- Sales Rank: #846576 in Books
- Published on: 2015-06-04
- Original language: English
- Number of items: 1
- Dimensions: 9.30" h x .90" w x 6.00" l, 1.62 pounds
- Binding: Hardcover
- 328 pages
Review
"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
About the Author
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.
Most helpful customer reviews
9 of 9 people found the following review helpful.
The basic idea of the book is good and such a book is very timely
By William Venables
The basic idea of the book is good and such a book is very timely. Matloff delivers with clarity and authority, which is good. The book suffers a lot, however, by the lack of a good professional edit. The leading example, though useful in containing many the features you would need as an illustrative example is, frankly, rather pedestrian and why anyone would want to do such a calculation is left unmotivated. The code listings in the book, of which there are a great number of course, are presented in a variable pitch roman font making them not only unattractive, but difficult to read quickly. It's very jarring. Worse still, though, is the large number of typos and other infelicities that mean you have to check constantly as you read. The book shows every sign of being published in a great rush, and it almost looks like the author is leaning on his readers to do the final edit! The book also has no Bibliography and the only inline references I could find were to the author's own previous book on The Art of R Programming.
5 of 5 people found the following review helpful.
Rush job in editing still provides very valuable information.
By Alexander Whitworth
Let me preface by saying that I consider Matloff's "The Art of R Programming" to be the best book on R available. So, I had very high hopes for this book. However, this book falls far short of the quality of Matloff's book on R.
In general, I greatly agree with Venables prior review on editing and typesetting. It appears that the printing was done in the basic font used in LaTeX documents made in RStudio or a basic LaTeX editor. In my mind, Matloff was done a huge disservice by CRC Press by not doing a professional edit and making appropriate typesetting decisions.
Moving to the book's content, I found the information very informative and useful. In particular Chapters 2 - 6 were great.
That said, given how much of this information is freely available online, it's hard to justify a purchase. In addition, this edition has changed very little from the rough draft that is freely available on Dr Matloff's website. Given the poor quality edit and the lack of new / revised material vs the free draft version, I have a hard time recommending this as a purchase, though I do consider it a valuable reference. Yes, I realize that that is seemingly contradictory.
1 of 1 people found the following review helpful.
especially since there are so many good parallel materials out there for free
By Amazon Customer
Not worth the money, especially since there are so many good parallel materials out there for free. The book felt rushed too, like it wasn't proofread enough times or something.
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