MPI Parallelism

MPI stands for 'Message Passing Interface', and over the last decade it has become the de-facto standard for running large scale computations on Linux clusters around the world. In most supercomputing centers this will be the ONLY option you have for running in parallel, and administrators may be actively hostile to trying to make use of any non-MPI software on their clusters.

MPI setup

• Mac - MPI is provided as part of the operating system, so we provide a fully functional binary. No extra installation should be required.

• Windows - we do not presently offer MPI support. try one of the other parallelism methods

• Linux - Unfortunately there are many variants of MPI and there are many variants of linux. Due to these issues, there is one specific file which we cannot distribute as part of the EMAN2 binary release for linux. The following will explain how to go about setting this up:

On linux clusters you will need to compile one small module directly on the cluster in question. In most cases this will be completely trivial, and the setup will be largely automatic. However, in some situations it may require you to do some research about your cluster and/or consult your cluster documentation.

The EMAN2 binary and source distributions both include a subdirectory called mpi_eman. Change to this directory. Inside you will find a 0README text file you may consult for details, but in many cases simply doing a :

make -f Makefile.linux2 install

will do everything that is necessary.

Specific MPI systems

• OpenMPI - This is the most widely used distribution at present. If your cluster uses version 1.2 or earlier of OpenMPI, it will likely work without difficulty. However, if you are using 1.3 or newer, you will need to make sure OpenMPI is compiled with the --disable-dlopen option or you will probably get fatal errors when you try to run the test scripts. You may need to talk to your system administrator if this happens. --disable-dlopen is required for Python compatibility, and is not an EMAN2 specific requirement.

• MPICH2/MVAPICH2 - Another very standard MPI library. Worked fine for us in initial testing, but we have not done extensive burn-in testing on it.
• LAM - An older library. We haven't tested it.
• Other proprietary MPI distributions - Many high-end clusters will have a commercial MPI installation to make optimal use of specific hardware. While EMAN2 should work fine with these systems, it is difficult to predict what problems you may encounter. Please contact us if you have any problems.

OpenPBS/Torque

If your cluster uses openPBS/Torque, there is an example batch file called pbs.example which you can edit and use for testing. There are also a couple of simple python test scripts which could be executed with mpirun. You will need to learn and understand how you are expected to launch MPI jobs on your specific cluster before trying any of these things ! If you just naively run some of these scripts you could do things which in some installations will make the system administrator very angry, so please, learn what you're supposed to do and how before proceeding past this point. If you do not know what you're doing, showing the pbs.example script to a knowledgeable user should tell them what they need to know before offering you advice on what to do.

Using MPI

Once you have verified that your MPI support is installed and working, making actual use of MPI to run your jobs is quite straightforward, with a couple of caveats.

1. Make sure you read this warning

2. Prepare the batch file appropriate for your cluster. Do not try to use 'mpirun' or 'mpiexec' on any EMAN programs. Instead, add the '--parallel=mpi:<n>:/path/to/scratch' option to an EMAN2 command like e2refine.py. Some commands do not support the --parallel option, and trying to run them using mpirun will not accomplish anything useful.

   • replace <n> with the total number of processors you have requested (these number must match exactly)

   • replace /path/to/scratch, with the path to a scratch storage directory available on each node of the cluster. Note that this directory must be local storage on each node, not a directory shared between nodes. If you use the path to a shared directory, like $HOME/scratch, you will have very very serious problems. You must use a filesystem local to the specific node. If you don't have this information, check your cluster documentation and/or consult with your system administrator.
   • Make sure that after the last e2* command in your batch script you put an 'e2bdb.py -c' command to make sure all of the output image files have been flushed to disk.
3. Immediately before submitting your job, run 'e2bdb.py -c'. This will require you to exit all running EMAN2 jobs (if any) before proceeding. Do this.
4. Submit your job.

5. IMPORTANT : While the job is running, you have effectively ceded control of that specific project to the cluster nodes using MPI. You MUST NOT modify any of the files in that project in any way while the job is running, or you will risk a variety of bad things. While the bad things will not always happen, there is a large risk, and the bad things are VERY bad, including corruption of your entire project. Wait until the job is complete before you do anything that could possibly change files in that directory.

6. When you run into problems (note I say when, not if), and you have exhausted any local MPI experts, please feel free to email me (sludtke@bcm.edu). Once you have things properly configured, you should be able to use MPI very routinely, but getting there may be a painful process on some clusters. Don't get too discouraged.

Note about use of shared clusters

EMAN2 can make use of MPI very efficiently, however, as this type of image processing is VERY data intensive, in some situations, your jobs may be limited by data transfer between nodes rather than by the computational capacity of the cluster. The inherent scalability of your job will depend quite strongly on the parameters of your reconstruction. In general larger projects will scale better than smaller projects, but projects can be 'large' in several different ways (eg- large box size, large number of particles, low symmetry,...). If your cluster administrator complains that your jobs aren't using the CPUs that you have allocated for your jobs sufficiently, you can try A) running on fewer processors, which will increase the efficiency (but also increase run-times), or you can refer them to me, and I will explain the issues involved. We are also developing tools to help better measure how efficiently your jobs are taking advantage of the CPUs you are allocating, but this will be an ongoing process.