README.Rmd

---
output: github_document
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```
# easypar <a href="https://caravagnalab.github.io/easypar/"><img src="man/figures/logo.png" align="right" height="139" /></a>


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`easypar` makes it easy to implement parallel computations in R. If youo have a function that 
carries out your desired computation, `easypar` will take care of the burden of  turning that function into a runnable parallel piece of R code. The package offers two possible solutions for parallelisation. It can generate a parallel function call exploiting the `foreach` and 
`doParallel` paradigms for parallel computing, or can generate a ready-to-use array job for the popular LSF (Platform Load Sharing Facility) and Slurm workload manages for distributed high performance computing.   With `easypar`, speeding up R computations through parallelism is a trivial task.

#### Help and support

[![](https://img.shields.io/badge/GitHub%20Pages-https://caravagnalab.github.io/easypar/-yellow.svg)](https://caravagnalab.github.io/easypar)

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### Installation

``` r
# install.packages("devtools")
devtools::install_github("caravagnalab/easypar")
```
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#### Copyright and contacts

Cancer Data Science (CDS) Laboratory, University of Trieste, Italy.

[![](https://img.shields.io/badge/CDS%20Lab%20Github-caravagnalab-seagreen.svg)](https://github.com/caravagnalab)
[![](https://img.shields.io/badge/CDS%20Lab%20webpage-https://www.caravagnalab.org/-red.svg)](https://www.caravagnalab.org/)