# Speed up R by using a different BLAS implementation

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It is no news that R’s default BLAS is much slower that other available BLAS implementations. In A trick to speed up R matrix calculation/ Yu-Sung Su recommends using the ATLAS BLAS which is available on CRAN. When I learned about the possible speed-up a while ago I tried several BLAS libraries and I found that GotoBLAS2 was giving me the best performance among the open-source BLAS implementations. Today I decided to check once again how much it makes sense to replace R’s default BLAS library.

Here are some results from my Intel i7-620M laptop running Windows 7:

I used *rbenchmark* for the measurements with the default of 100 repetitions and I saved and reused the same matrix across all runs:

```
A = matrix(rnorm(1000*1000),ncol=1000)
benchmark(A %*% A)
benchmark(svd(A))
```

### A %*% A

BLAS | seconds | speed-up | |
---|---|---|---|

MKL | 31.65 | 7.16 | |

GotoBLAS2 | 42.30 | 5.36 | |

Default | 226.53 | 1.00 |

### svd(A)

BLAS | seconds | speed-up | |
---|---|---|---|

MKL | 317.61 | 12.52 | |

GotoBLAS2 | 389.13 | 10.22 | |

Default | 3976.21 | 1.00 |

## Conclusion

If you are using R operations that rely on BLAS than you should always use a faster BLAS implementation. If you are using Windows, you can use the Rblas.dll from here or you try the Revolution R Community edition which comes with a MKL BLAS.

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