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 observations, p variables" matrix format but also from time series,
[129] L. R. Scott, J. M. Boyle, and B. Bagheri. Distributed data structures for scientific ... [136] Marc Snir, Steve W. Otto, Steven Huss-Lederman, David W. Walker, and Jack Dongarra. MPI: The Complete Reference. MIT Press, 1995.
What you will learnUse Python to read and transform data into different formatsGenerate basic statistics and metrics using data on diskWork with computing tasks distributed over a clusterConvert data from various sources into storage or ...
This book offers an overview of some of the most prominent parallel programming models used in high-performance computing and supercomputing systems today.
This book integrates the core ideas of deep learning and its applications in bio engineering application domains, to be accessible to all scholars and academicians.
This first part closes with the MapReduce (MR) model of computation well-suited to processing big data using the MPI framework. In the second part, the book focuses on high-performance data analytics.
"This book is about the fundamentals of R programming.
This concise book introduces you to several strategies for using R to analyze large datasets, including three chapters on using R and Hadoop together.
First, we'll derive a new DataFrame by applying a filter to our original DataFrame that removes all people with the last name Williams. We'll then inspect the makeup of the new DataFrame by using the same map_partitions call to count ...
A Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book.
Vector Models for Data-Parallel Computing describes a model of parallelism that extends and formalizes the Data-Parallel model on which the Connection Machine and other supercomputers are based. It presents many...