Optimizing R Code for High-Performance Computing
Regarding applications related to data analysis and statistics, the programming language R is a pretty popular one in the industry. However, when dealing with large datasets or other facets regarding complex computations, R can be tweaked using some tools to increase performance. By reading this guide, people can learn how R can affect high-performance computing (HPC) and the various tools and techniques they can use to optimize R code for such HPC applications.
A Brief Overview Of High-Performance Computing
HPC or commonly known as High-Performance Computing, can be defined as the ability to process loads of data to perform complicated calculations. Frequently, these complex functions need to be performed at high speeds, and it’s a groundbreaking attribute when one considers the field of data science and data analysis. From engineering and finance to research, HPC solutions are widely popular. And with data science, HPC solutions provide the power, and there can be no doubt that R excels in this field.
How Is R Relevant In High-Performance Computing?
The programming language of R is widely used by programmers and data scientists, owing to its benefits in statistical analysis and efficiency in visualizing data. Scientists can make use of R to analyze large complex datasets. Additionally, R has a large and diverse community of developers with loads of libraries and packages that people can exploit. However, its most beneficial aspect is that R is open source, and it can be tailored to meet specific needs, making it a viable tool for HPC solutions.
Tools and Techniques To Optimize R Code for HPC
Upgrade Your Hardware Systems:
Understand that memory is the most important consideration here, but if possible, go for the best. Although R can only use one core at a time internally, generally speaking, try to get more cores.
Make Use of Parallel Processing:
Users can use all the available cores or multiple hard drives. The CRAN package foreach() function has a lot of easy-to-use tools that can help execute R code for HPC solutions in parallel.
Considering The Data Involved:
Try to minimize the number of copies of the data used and process them in chunks to increase efficiency. It might be beneficial to take advantage of integers and store and read data that is needed.
Make Use Of Optimized Libraries:
Make use of the R code’s vast ecosystem of libraries, codes and packages for specific applications or tasks. Better to use such libraries that are optimized or tweaked for high performance rather than starting over to create a bland code from scratch.
We hope that this tutorial will come in handy when looking to tune up the performance of HPC using R-code. Check out more of our resources on our website Education Nest to learn more about optimizing R code. Education Nest is a subsidiary of Sambodhi Research and Communications Pvt. Ltd. It is a global knowledge exchange platform that aims at empowering learners with data-driven skills to help with decision making. It has lots of online courses curated by experts to help learners expand their skills and engage with a worldwide community of like-minded professionals through live training sessions, various case-based learning, and interactions with the best in the field.