This repository contains the seminar report and associated materials for the course "Newest Trends in High-Performance Data Analytics" at Georg-August-Universität Göttingen. The report investigates the use of R in high-performance data analytics (HPDA), focusing on memory management, GPU computing, parallel processing, and benchmarking.
├── README.md
├── 2024-03-25_R_HPDA_Luebbers.pdf # Detailed insights into leveraging R for high-performance data analytics
├── NTHPDA.Rmd # R notebook containing example code and benchmarks
- Memory Management: Techniques to optimize R's memory usage for handling large datasets.
- GPU Computing: Utilizing GPU for accelerated computations with R packages.
- Parallel Processing: Methods to perform parallel computations to speed up data processing tasks.
- Benchmarking: Evaluating the performance of various R functions and comparing them with Python.
- Leveraging C++: Enhancing R's performance by integrating C++ code.
- Computational Biology: Using R for high-performance data analysis in genomics and bioinformatics.
- Comparative Analysis: Evaluating R's performance against Python for various data processing tasks.
To run the example scripts, you need to have R installed on your system along with the necessary packages. You can install the required packages using the following commands:
- Install the required packages
install.packages(c("forcats", "readr", "dplyr", "tidyr", "ggplot2", "tibble", "devtools"))- Download the data
The data can be found here: https://ftp.cdc.gov/pub/Health_Statistics/NCHS/Datasets/DVS/natality/Nat2018us.zip
- Knit the R Markdown file
Open the NTHPDA.Rmd file in RStudio and click the "Knit" button to generate the HTML report.
Alternatively, you can use the following command in your R console:
rmarkdown::render("NTHPDA.Rmd")Making gpuR work :)
This project is licensed under the MIT License. See the LICENSE file for details.
For any questions or feedback, please contact Christopher L. Lübbers at c.luebbers@stud.uni-goettingen.de.