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Parallel Computing for Data Science

With Examples in R, C++ and Cuda

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  • 328 pages
  • 12 hours of reading

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Focusing on parallel data structures and algorithms, this book serves as a comprehensive guide for those interested in parallel computing within data science. It equips readers with the skills to write effective parallel code across multiple programming languages and explores various R packages and tools. The content includes discussions on the classic "n observations, p variables" matrix format, alongside common data structures, complemented by numerous examples that highlight the challenges faced in parallel programming.

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Parallel Computing for Data Science, Norman Matloff

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Released
2015
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