Parallel Computing for Data Science : With Examples in R, C++ and CUDA

By: Matloff, NormanMaterial type: TextTextSeries: eBooks on DemandChapman & Hall/CRC The R Series: Publisher: Hoboken : CRC Press, 2015Description: 1 online resource (336 p.)ISBN: 9781466587038Subject(s): Computer science | High performance computing | Parallel processing (Electronic computers)Genre/Form: Electronic books.Additional physical formats: Print version:: Parallel Computing for Data Science : With Examples in R, C++ and CUDADDC classification: 004.2039477 LOC classification: QA76.58 .M384 2015Online resources: Click here to view this ebook.
Contents:
Front Cover; Contents; Preface; Author's Biography; Chapter 1: Introduction to Parallel Processing in R; Chapter 2: ""Why Is My Program So Slow?"": Obstacles to Speed; Chapter 3: Principles of Parallel Loop Scheduling; Chapter 4: The Shared-Memory Paradigm: A Gentle Introduction via R; Chapter 5: The Shared-Memory Paradigm: C Level; Chapter 6: The Shared-Memory Paradigm: GPUs; Chapter 7: Thrust and Rth; Chapter 8: The Message Passing Paradigm; Chapter 9: MapReduce Computation; Chapter 10: Parallel Sorting and Merging; Chapter 11: Parallel Pre x Scan; Chapter 12: Parallel Matrix Operations
Chapter 13: Inherently Statistical Approaches: Subset MethodsAppendix A: Review of Matrix Algebra; Appendix B: R Quick Start; Appendix C: Introduction to C for R Programmers; Back Cover
Summary: 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 platfor
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Front Cover; Contents; Preface; Author's Biography; Chapter 1: Introduction to Parallel Processing in R; Chapter 2: ""Why Is My Program So Slow?"": Obstacles to Speed; Chapter 3: Principles of Parallel Loop Scheduling; Chapter 4: The Shared-Memory Paradigm: A Gentle Introduction via R; Chapter 5: The Shared-Memory Paradigm: C Level; Chapter 6: The Shared-Memory Paradigm: GPUs; Chapter 7: Thrust and Rth; Chapter 8: The Message Passing Paradigm; Chapter 9: MapReduce Computation; Chapter 10: Parallel Sorting and Merging; Chapter 11: Parallel Pre x Scan; Chapter 12: Parallel Matrix Operations

Chapter 13: Inherently Statistical Approaches: Subset MethodsAppendix A: Review of Matrix Algebra; Appendix B: R Quick Start; Appendix C: Introduction to C for R Programmers; Back Cover

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 platfor

Description based upon print version of record.

Author notes provided by Syndetics

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.

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