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

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

Volumn , Issue , 2015, Pages 1-311

  Matloff, Norman ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPLICATION PROGRAMS; COMPUTER GRAPHICS; GRAPH THEORY; GRAPHICS PROCESSING UNIT; MULTICORE PROGRAMMING; PARALLEL PROGRAMMING; PROGRAM PROCESSORS;

MULTI-CORE SYSTEMS; NETWORK GRAPH; PARALLEL DATA STRUCTURES;

C++ (PROGRAMMING LANGUAGE);

EID: 85053100576     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1201/b18549     Document Type: Book

References (12)

1. An Introduction to Computer Systems, N. Matloff, http://heather.cs.ucdavis.edu/~matloff/50/PLN/CompSystsBook.pdf.
2. Barrier implementation is discussed in many books, such as The Art of Concurrency, by Clay Breshears, O'Reilly, 2009.
3. That book also discusses “strategy" for writing parallel code, as does Patterns for Parallel Programming, by T. Mattson et al, Addison-Wesley, 2004.
4. A number of books cover OpenMP in detail, such as Multicore Application Programming, by Darryl Gove, Addison-Wesley, 2011
5. For TBB, consult Intel Threading Building Blocks, by James Reinders, O'Reilly, 2007.
6. The author highly recommends the excellent case study, Histogram Calculation in CUDA, by Victor Podlozhnyuk of NVIDIA, 2007, at http://developer.download.nvidia.com/compute/cuda/1.1-Beta/x86_website/projects/histogram64/doc/histogram.pdf.
7. Several good guides are available, including Parallel Programming with MPI, by Peter Pacheco, Morgan Kaufman, 1997.
8. A very accessible treatment is Algorithms Sequential and Parallel, by R. Miller and L. Boxer, Prentice-Hall, 2000
9. The reader would do well by beginning with Numerical Linear Algebra on High-Performance Computers, by J. Dongarra et al, SIAM, 1998.
10. A good compendium of graph algorithms is Practical Graph Mining with R, by N. Samatova et al, CRC, 2014.
11. see “Software Alchemy: Turning Complex Statistical Computations into Embarrassingly-Parallel Ones," N. Matloff, to appear in the Journal of Statistical Software, 2015.
12. The Bag of Little Bootstraps is described in “A Scalable Bootstrap for Massive Data," A. Kleiner, A. Talwalkar, P. Sarkar and M.I. Jordan, Journal of the Royal Statistical Society, Series B, 2013.