Parallel Computing For Data Science

This publication enables readers to handle more complex bioinformatics applications and larger and richer data sets. As the editor clearly shows, using powerful parallel computing tools can lead to significant breakthroughs in deciphering genomes, understanding genetic disease, designing customized drug therapies, and understanding evolution. A broad range of bioinformatics applications is covered with demonstrations on how each one can be parallelized to improve performance and gain faster rates of computation. Current parallel computing techniques and technologies are examined, including distributed computing and grid computing.

Learn Mathematics and Statistics of Machine Learning, Artificial Intelligence, Neural Networks and Deep Learning

Authors: Nielsen, Frank
Contains numerous exercises and a test exam
Features material that has been used and tested with students
Provides additional material, including source C++/MPI codes and slides for each chapter, on an accompanying website

This publication enables readers to handle more complex bioinformatics applications and larger and richer data sets. As the editor clearly shows, using powerful parallel computing tools can lead to significant breakthroughs in deciphering genomes, understanding genetic disease, designing customized drug therapies, and understanding evolution.

R Programming, RStudio, Parallel Computing, Multicore, Data Science, Speed Optimization

Unleash the capabilities of Python and its libraries for solving high performance computational problems.