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, network graph models, and numerous other structures common in data science. The examples illustrate the range of issues encountered in parallel programming.With the main focus on computation, the book shows how to compute on three types of platfo
. Most data-parallel languages use arrays to support parallelism. This regular data structure allows...
R is a mature open-source programming language for statistical computing and graphics. Many areas of...
The success of parallel architectures has been limited by the lack of high-level parallel programmin...
This is a draft of the first half of a book to be published in 2014 under the Chapman & Hall imp...
R is a mature open-source programming language for statistical computing and graphics. Many areas of...
R is a mature open-source programming language for statistical computing and graphics. Many areas of...
Scientific computing is by its very nature a practical subject- it requires tools and a lot of pract...
Brief overview of the recent general tasks for parallel computation on graphics processing units is ...
[[abstract]]The basic concept of piplined data-parallel algorithms is introduced by contrasting the ...
In the 1990s the Beowulf project smoothed to way for massively paral-lel computing as access to para...
This paper shows how a high level matrix programming language may be used to perform Monte Carlo sim...
This paper shows how a high level matrix programming language may be used to perform Monte Carlo sim...
Parallel computers with tens of thousands of processors are typically programmed in a data parallel ...
Our aim in this book is to present and enlarge upon those aspects of parallel computing that are nee...
Programming Massively Parallel Processors discusses basic concepts about parallel programming and GP...
. Most data-parallel languages use arrays to support parallelism. This regular data structure allows...
R is a mature open-source programming language for statistical computing and graphics. Many areas of...
The success of parallel architectures has been limited by the lack of high-level parallel programmin...
This is a draft of the first half of a book to be published in 2014 under the Chapman & Hall imp...
R is a mature open-source programming language for statistical computing and graphics. Many areas of...
R is a mature open-source programming language for statistical computing and graphics. Many areas of...
Scientific computing is by its very nature a practical subject- it requires tools and a lot of pract...
Brief overview of the recent general tasks for parallel computation on graphics processing units is ...
[[abstract]]The basic concept of piplined data-parallel algorithms is introduced by contrasting the ...
In the 1990s the Beowulf project smoothed to way for massively paral-lel computing as access to para...
This paper shows how a high level matrix programming language may be used to perform Monte Carlo sim...
This paper shows how a high level matrix programming language may be used to perform Monte Carlo sim...
Parallel computers with tens of thousands of processors are typically programmed in a data parallel ...
Our aim in this book is to present and enlarge upon those aspects of parallel computing that are nee...
Programming Massively Parallel Processors discusses basic concepts about parallel programming and GP...
. Most data-parallel languages use arrays to support parallelism. This regular data structure allows...
R is a mature open-source programming language for statistical computing and graphics. Many areas of...
The success of parallel architectures has been limited by the lack of high-level parallel programmin...