Tag Archives: parallel

Turns out setting up and using MrBayes on an Ubuntu system is much easier than I had thought. If all you want is the normal (serial) version of MrBayes, you can just download it from the repositories. However, if you want a serious speed up in the time it takes to get a good result, you can also run it in parallel on a multicore system (that is, pretty much any computer made in the last 4 years). To get it set up and running on Linux, I used some information I found in a forum post. To recap from there:

1. Install the parallel libraries you need from the repositories. The package names I used were: mpich2, libmpich2-dev, and libmpich2-1.2, and libreadline6-dev.
2. Download the source code file for MrBayes and unarchive it (on Ubuntu you can just right-click and select ‘Extract Here’
3. Find the ‘Makefile’ in the source code and change the line that says ‘MPI ?= no’ so that it says ‘MPI = yes’
4. Open a terminal, and navigate to the MrBayes folder (e.g. type in ‘cd /path/to/folder/mrbayes-3.1.2/’) and then make the package (type ‘Make’ at the prompt).  It might also be a good idea to change the file called ‘mb’ that is created to something like ‘mbpar’ so that you know it’s the parallel version. Also, I needed to make the file executable, so I typed ‘chmod +x mbpar’  to do that.
5. Now, you’ll need to create a file in your home folder called ‘.mpd.conf’ with the line MPD_SECRETWORD=<secretword> in it. Change the <secretword> to something else though; it can be pretty much any word you like.
6. ‘mpd &’ will launch the MPICH daemon, which needs to be running in order to handle communicating between the different cores.
7. After all this, I was able to type ‘mpirun -np 2 /path/to/mrbayes/mbpar’ to run the parallel version of MrBayes in parallel on both cores of my dual core system. If you have more cores, you can always change the -np argument (e.g. to run it using 4 cores, type ‘mpirun -np 4 /path/to/mrbayes/mbpar’)

With the few tests I’ve done so far, I’ve seen about a 80% speed up by just using 2 cores instead of 1. It’s nice not to have to wait nearly so long to get my results. We’ll see what kind of time savings this could bring if I did it on an 8-core computer.