Parallel computing for data science : with examples in R, C++ and CUDA
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton, Florida :
CRC Press ,
c2016
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| Series: | Chapman & Hall/CRC: The R Series
28 |
| Subjects: |
Table of Contents:
- 1. Introduction to parallel processing in R
- 2. "Why is my program so slow?'' obstacles to speed
- 3. Principles of parallel loop scheduling
- 4. The shared-memory paradigm; a gentle introduction via R
- 5. The shared-memory paradigm: C level
- 6. The shared-memory paradigm: GPU's
- 7. Thrust and Rth
- 8. The message passing paradigm
- 9. Mapreduce computation
- 10. Parallel sorting and merging
- 11. Parallel prefix scan
- 12. Parallel matrix operations
- 13. Inherently statistical approaches; subset methods