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Parallel computing for data science : with examples in R, C++ and CUDA /
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 observ...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Boca Raton, FL :
CRC Press,
[2016]
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| Colección: | Chapman & Hall/CRC the R series (CRC Press)
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- 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.