ACCLAiM: MPI Collective Autotuning for Exascale

Welcome! ACCLAiM is a tool that autotunes MPICH's collective algorithm selection at allocation time. The goal of ACCLAiM is to transparently improve the performance of large-scale HPC workloads.

Dependencies

• Python3 (Tested with v3.13)
  • For specific packages, see Setup
• MPICH (Tested with v4.3.0)
  • For install instructions, see the MPICH README

Setup

To setup the ACCLAiM tool, run the provided setup script setup.py with the following arguments:

• <mpich_path>: The path to the MPICH installation directory
• <system_type>: The type of system used for parallel scheduling. The following options are available:
  • polaris for ANL's Polaris cluster
  • serial disables the parallel scheduler (recommended for all other systems)
  • local allows an infinite number of parallel microbenchmarks. Use for testing purposes only.
• [max_ppn] (optional): The maximum number of processes per node for a single microbenchmark run.

Note: The max_ppn argument is optional. The provided value will be ignored for system=polaris. For local, the default is 8, and for serial, the default is 64.

setup.py checks for all required Python packages and will fail if they are not detected. If this occurs, install the requested package and re-run the script.

When it completes successfully, setup.py creates config.ini at the root directory of your repository. Review all of the settings and confirm they are correct.

Known Issues

When installing the OSU microbenchmarks on Mac systems, there may be a compilation error because Mac OS does not implement pthreads barrier. To work around this issue, We have successfully compiled the benchmarks by adding an implementation to `osu_microbenchmarks/c/util/osu_util_mpi.h/c. See this patch for an example implementation.

Publication

ACCLAiM is the result of this paper. Please check it out for more details.

To refer to ACCLAiM, please use the following citation:

@inproceedings{acclaim,
  author={Wilkins, Michael and Guo, Yanfei and Thakur, Rajeev and Dinda, Peter and Hardavellas, Nikos},
  booktitle={2022 IEEE International Conference on Cluster Computing (CLUSTER)}, 
  title={ACCLAiM: Advancing the Practicality of MPI Collective Communication Autotuning Using Machine Learning}, 
  year={2022},
  pages={161-171},
  doi={10.1109/CLUSTER51413.2022.00030}
}