A Memory-Driven Mapping Algorithm for Heterogeneous Systems

mpibind is a memory-driven algorithm to map parallel hybrid applications to the underlying hardware resources transparently, efficiently, and portably. Unlike other mappings, its primary design point is the memory system, including the cache hierarchy. Compute elements are selected based on a memory mapping and not vice versa. In addition, mpibind embodies a global awareness of hybrid programming abstractions as well as heterogeneous devices such as accelerators.

Getting Started

This project uses GNU Autotools.

$ ./bootstrap

$ ./configure --prefix=<install_dir>

$ make

$ make install

The resulting library is libmpibind and a simple program using it is src/main.c

Contributing

Contributions for bug fixes and new features are welcome and follow the GitHub fork and pull model. Contributors develop on a branch of their personal fork and create pull requests to merge their changes into the main repository.

The steps are similar to those of the Flux framework:

1. Fork mpibind.
2. Clone your fork: git clone git@github.com:[username]/mpibind.git
3. Create a topic branch for your changes: git checkout -b new_feature
4. Create feature or add fix (and add tests if possible)
5. Make sure everything still passes: make check
6. Push the branch to your GitHub repo: git push origin new_feature
7. Create a pull request against mpibind and describe what your changes do and why you think it should be merged. List any outstanding todo items.

Authors

mpibind was created by Edgar A. León.

Citing mpibind

To reference mpibind in a publication, please cite one of the following papers:

• Edgar A. León and Matthieu Hautreux. Achieving Transparency Mapping Parallel Applications: A Memory Hierarchy Affair. In International Symposium on Memory Systems, MEMSYS'18, Washington, DC, October 2018. ACM.

• Edgar A. León. mpibind: A Memory-Centric Affinity Algorithm for Hybrid Applications. In International Symposium on Memory Systems, MEMSYS'17, Washington, DC, October 2017. ACM.

• Edgar A. León, Ian Karlin, and Adam T. Moody. System Noise Revisited: Enabling Application Scalability and Reproducibility with SMT. In International Parallel & Distributed Processing Symposium, IPDPS'16, Chicago, IL, May 2016. IEEE.

Other references:

• J. P. Dahm, D. F. Richards, A. Black, A. D. Bertsch, L. Grinberg, I. Karlin, S. Kokkila-Schumacher, E. A. León, R. Neely, R. Pankajakshan, and O. Pearce. Sierra Center of Excellence: Lessons learned. In IBM Journal of Research and Development, vol. 64, no. 3/4, May-July 2020.

• Edgar A. León. Cross-Architecture Affinity of Supercomputers. In International Supercomputing Conference (Research Poster), ISC’19, Frankfurt, Germany, June 2019.

• Edgar A. León. Mapping MPI+X Applications to Multi-GPU Architectures: A Performance-Portable Approach. In GPU Technology Conference, GTC'18, San Jose, CA, March 2018.

Bibtex file.

License

mpibind is distributed under the terms of the MIT license. All new contributions must be made under this license.

See LICENSE and NOTICE for details.

SPDX-License-Identifier: MIT.

LLNL-CODE-812647.