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MPI cluster on Docker Swarm

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# docker-mpi

MPI cluster on Docker Swarm.

Another (more complete solution) is available here [https://github.com/NLKNguyen/alpine-mpich](https://github.com/NLKNguyen/alpine-mpich)

Do you have two or more physical machines and want to try your personal MPI cluster for your calculations? This toy project might be useful for you! Setup your Docker Swarm cluster, deploy mpistack image, attach to the master container and simply run your MPI program on multiple hosts.

# Prerequisites
Those things you need to do only once.

Setup Docker Sworm network
(details: TBD)

Set up registry container (this is the approach to distribute image among nodes):
```
docker service create --name registry --publish published=5000,target=5000 registry:2
```
Now we are ready to push your image there.

# Launching MPI cluster

Build and push stackmpi image to the registry:
```
docker-compose build
docker-compose push
```
stackmpi is besed on Ubuntu 20.04 with MPICH installed. If you need any additianal software available on each node, modify Dockerfile appropriately and build+push image again.
REMARK: If you restart your machine, from some reason the image needs to be pushed to the repository again.

There are a few helper scripts available:
`start-stack` and `stop-stack` starts or stops all nodes (docker stack) specified in docker-compose.yml. By default there is one _master_ node started on _manager_ node (assume this is current one) and 2 _worker_ nodes. If you want more workers, go to docker-compose.yml file and modify `replicas` attribute in `worker` section.

`attach-stack` attaches to the _master_ container. The another approach is to SSH to the container with `ssh-stack` but by default SSH port is not exposed. You can do this by modification docker-compose.yml (uncomment `port` section in `master` section).

Inside master node, there are a few aditional commands available:
`node-master` displays IP of master node,
`node-workers` displays list of IPs of worker nodes,
`machines` get list of IPs of all nodes (master and workers) and put it into the file `/root/machines` (yep, not perfect),
`sync` replicates directory `/root/project/` to all nodes at the same location (synchronization of nodes using `rsync`). Directory `docker-mpi/project/` (`docker-mpi` repository) is mount as a volume to `/root/project/` and acts as a directory for your project. You should put here binaries of your program and any necessary resources.

# Executing MPI program
Attach to the master container with `attach-stack` command and fill the file `/root/machines` with the list of IPs of all nodes with `machines` command. Now you are ready to run simplest MPI program:
```
mpiexec -f machinefile -n 3 hostname
```
The output might look like this
```
root@32c03a788550:~# mpiexec -f machinefile -n 3 hostname
32c03a788550
399a9293ebdb
7688c165e99f
```

Now lets try something closer to the reality. Go to `/root/project/` and build sample program with `make`, and synchronized project directory with all nodes (`sync` command). Exacute the program with:
```
mpiexec -f machinefile -n 3 project/test-mpi
```
The output might look like this
```
root@32c03a788550:~# mpiexec -f machinefile -n 3 project/test-mpi
Processor 399a9293ebdb, rank 2 out of 3 processors. CPUs: 2   CPUs available: 2
Processor 32c03a788550, rank 0 out of 3 processors. CPUs: 8   CPUs available: 8
Processor 7688c165e99f, rank 1 out of 3 processors. CPUs: 4   CPUs available: 4
```

docker-mpi

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