R/parallel: Speeding Up Bioinformatics Analysis with R
Author Information
Author(s): Vera Gonzalo, Jansen Ritsert C, Suppi Remo L
Primary Institution: Groningen Bioinformatics Centre (GBiC), University of Groningen
Hypothesis
Can parallel computing technologies be effectively integrated into R to reduce data processing time for bioinformaticians?
Conclusion
R/parallel saves bioinformaticians time in analyzing experimental data by automating parallel execution and reducing processing time.
Supporting Evidence
- R/parallel can reduce processing time by N-fold, where N is the number of available processor cores.
- The tool allows bioinformaticians to automate parallel execution of loops easily.
- R/parallel can be integrated directly with existing R packages without changing implemented algorithms.
Takeaway
R/parallel is a tool that helps scientists analyze data faster by using multiple computer cores at the same time, making their work easier.
Methodology
An R add-on package was designed to enable user-friendly parallel computing capabilities without requiring changes to existing algorithms.
Limitations
The package may not perform optimally for very small tasks due to overhead from parallelization.
Digital Object Identifier (DOI)
Want to read the original?
Access the complete publication on the publisher's website