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This Week's Multicore Reading List


A list of book releases compiled by Dr. Dobb's to keep you up-to-date on parallel programming and multicore technology.



Paradigms for Fast Parallel Approximability
by Josep Diaz, Maria Serna, Paul Spirakis, and Jacobo Toran
This book is a survey of the basic techniques for approximating combinatorial problems using parallel algorithms. Its core is a collection of techniques that can be used to provide parallel approximations for a wide range of problems, such as flows, coverings, matchings, traveling salesman problems, and graphs. For added clarity, the authors provide an introductory chapter containing the basic definitions and results. A final chapter deals with problems that cannot be approximated, and the book is rounded off by an appendix that gives a convenient summary of the problems described in the book. http://www.cambridge.org/us/catalogue/catalogue.asp?isbn=0521117925

Software for Data Analysis: Programming with R
by John M. Chambers
John Chambers has been the principal designer of the S language since its beginning, and in 1999 received the ACM System Software award for S, the only statistical software to receive this award. He is author or coauthor of the landmark books on S. Now he turns to R, the enormously successful open-source system based on the S language. R's international support and the thousands of packages and other contributions have made it the standard for statistical computing in research and teaching. This book guides the reader through programming with R, beginning with simple interactive use and progressing by gradual stages, starting with simple functions. More advanced programming techniques can be added as needed, allowing users to grow into software contributors, benefiting their careers and the community. R packages provide a powerful mechanism for contributions to be organized and communicated. The techniques covered include such modern programming enhancements as classes and methods, namespaces, and interfaces to spreadsheets or databases, as well as computations for data visualization, numerical methods, and the use of text data.
http://www.springer.com/statistics/computational/book/978-0-387-75935-7


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