![cudalaunch nvprof cudalaunch nvprof](https://campus.barracuda.com/resources/attachments/image/79462679/1/cudalaunch_dt_14.png)
The second approach is to use the GPU through CUDA directly. On the other hand, the number of GPU packages is currently limited, quality varies, and only a few domains are covered.
Cudalaunch nvprof install#
These packages are very easy to install and use. Examples include gputools and cudaBayesreg. The first approach is to use existing GPU-accelerated R packages listed under High-Performance and Parallel Computing with R on the CRAN site.
![cudalaunch nvprof cudalaunch nvprof](https://files.speakerdeck.com/presentations/c4210f2fa76e4a59abeb690cf39d3d8c/slide_48.jpg)
In this article, I will introduce the computation model of R with GPU acceleration, focusing on three topics: This way, R users can benefit from R’s high-level, user-friendly interface while achieving high performance. Therefore, R applications stand to benefit from GPU acceleration. R programs tend to process large amounts of data, and often have significant independent data and task parallelism.
![cudalaunch nvprof cudalaunch nvprof](https://files.speakerdeck.com/presentations/c4210f2fa76e4a59abeb690cf39d3d8c/slide_45.jpg)
However, R, like many other high-level languages, is not performance competitive out of the box with lower-level languages like C++, especially for highly data- and computation-intensive applications. Many domain experts and researchers use the R platform and contribute R software, resulting in a large ecosystem of free software packages available through CRAN (the Comprehensive R Archive Network). R is a free software environment for statistical computing and graphics that provides a programming language and built-in libraries of mathematics operations for statistics, data analysis, machine learning and much more.