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UW Research Computing

Running MPI Jobs

This guide describes when and how to run multi-core jobs, programmed with MPI, in CHTC’s high throughput compute (HTC) system.

To best understand the below information, users should already have an understanding of:

Overview

Before you begin, review our below discussion of MPI requirements and use cases, to make sure that our multi-core MPI capabilities are the right solution for your computing problem. If you have any questions, contact a CHTC facilitator at chtc@cs.wisc.edu.

Once you know that you need to run multi-core jobs that use MPI on our HTC system, you will need to do the following:

  1. Compile your code using our MPI module system
  2. Create a script to that loads the MPI module you used for compiling, and then runs your code
  3. Make sure your submit file has certain key requirements

A. Requirements and Use Cases

Most jobs on CHTC's HTC system are run on one CPU (sometimes called a "processor", or "core") and can be executed without any special system libraries. However, in some cases, it may be advantageous to run a single program on multiple CPUs (also called multi-core), in order to speed up single computations that cannot be broken up and run as independent jobs. If you have questions about the advantages and disadvantages of running multi-core jobs versus single-core jobs, contact one of CHTC's facilitators at chtc@cs.wisc.edu.

Running on multiple CPUs can be enabled by the parallel programming standard MPI. For MPI jobs to compile and run, CHTC has a set of MPI tools installed to a shared location that can be accessed via software modules.

B. View MPI Modules on the HTC System

MPI tools are accessible on the HTC system through software "modules", which are tools to access and activate a software installation. To see which MPI packages are supported in the HTC, you can type the following command from the submit server:

[alice@submit]$ module avail

Your software may require newer versions of MPI libraries than those available via our modules. If this is the case, send an email to chtc@cs.wisc.edu, to find out if we can install that library into the module system.

C. Submitting MPI jobs

1. Compile MPI Code

You can compile your program by submitting an interactive build job to one of our compiling servers. Do not compile code on the submit server, as doing so may cause performance issues. The interactive job is essentially a regular HTCondor job, but without an executable; you are the one running the commands instead (in this case, to compile the program).

Instructions for submitting an interactive build/compile job are available on our interactive submission guide. The only line in the submit file that you need to change is transfer_input_files to reflect all the source files on which your program depends. Otherwise, go through the steps described in that guide until immediately after running condor_submit -i.

Once your interactive job begins on one of our compiling servers, you can confirm which MPI modules are available to you by typing:

[alice@build]$ module avail

Choose the module you want to use and load it with the following command:

[alice@build]$ module load mpi_module

where mpi_module is replaced with the name of the MPI module you'd like to use.

IMPORTANT NOTICE: Make sure you are using a current MPI module; shown in the first section of modules under the /software/chtc/modules heading.

After loading the module, compile your program. If your program is organized in directories, make sure to create a tar.gz file of anything you want copied back to the submit server. Once typing exit the interactive job will end, and any *files* created during the interactive job will be copied back to the submit location for you.

If your MPI program is especially large (more than 100 MB, compiled), or if it can only run from the exact location to which it was installed, you may also need to take advantage of CHTC's shared software location or our public web proxy called Squid. Email CHTC's Research Computing Facilitators at chtc@cs.wisc.edu if this is the case.

2. Script For Running MPI Jobs

To run your newly compiled program within a job, you need to write a script that loads an MPI module and then runs the program, like so:

#!/bin/bash

# Commands to enable modules, and then load an appropriate MP/MPI module
export PATH
. /etc/profile.d/modules.sh
module load mpi_module

# Untar your program installation, if necessary
tar -xzf my_install.tar.gz

# Command to run your OpenMP/MPI program
# (This example uses mpirun, other programs
# may use mpiexec, or other commands)
mpirun -np 8 ./path/to/myprogram

Replace mpi_module with the name of the module you used to compile your code, myprogram with the name of your program, and X with the number of CPUs you want the program to use. There may be additional options or flags necessary to run your particular program; make sure to check the program's documentation about running multi-core processes.

3. Submit File Requirements

There are several important requirements to consider when writing a submit file for multicore jobs. They are shown in the sample submit file below and include:

  • Require access to MPI modules. To ensure that your job will have access to CHTC software modules, including MPI modules, you must include the following in your submit file.

    requirements = (HasChtcSoftware == true)
  • Request *accurate* CPUs and memory Run at least one test job and look at the log file produced by HTCondor to determine how much memory and disk space your multi-core jobs actually use. Requesting too much memory will cause two issues: your jobs will match more slowly and they will be wasting resources that could be used by others. Also, the fewer CPUs your jobs require, the sooner you'll have more jobs running. Jobs requesting 16 CPUs or less will do best, as nearly all of CHTC's servers have at least that many, but you can request and use up to 36 CPUs per job.
  • The script you wrote above (shown as run_mpi.sh below) should be your submit file "executable", and your compiled program and any files should be listed in transfer_input_files.

A sample submit file for multi-core jobs is given below:

# multicore.sub
# A sample submit file for running a single multicore (8 cores) job

## General submit file options
universe = vanilla
log = mc_$(Cluster).log
output = mc_$(Cluster).out
error = mc_$(Cluster).err

## Submit file options for running your program
executable = run_mpi.sh
# arguments = (if you want to pass any to the shell script)
should_transfer_files = YES
when_to_transfer_output = ON_EXIT
transfer_input_files = (this should be a comma separate list of input files if needed)

# Requirement for accessing new set of modules
requirements = ( HasChtcSoftware == true ) 

## Request resources needed by your job
request_cpus = 8
request_memory = 8GB
request_disk = 2GB

queue

After the submit file is complete, you can submit your jobs using condor_submit.