Hpmoon

Hpmoon is a parallel and distributed multi-objective genetic algorithm to EEG classification. The evolutionary procedure corresponds to an island-based model whose subpopulations are distributed among the nodes of a cluster. The application is able to parallelize the evaluation of the individuals using all the CPU-GPU devices simultaneously, which provides up to 4 levels of parallelism.

Requirements

Hpmoon requires a GCC compiler and OpenCL 1.2 compliant CPU-GPU devices. It also depends on the following APIs and libraries:

• OpenMPI.
• AMD APP SDK v2.9.1 or later.
• Doxygen if you want to generate documentation.

Documentation

Hpmoon is fully documented in its Github Pages. In addition, the Makefile file contains a rule to generate Doxygen documentation in the docs/html folder.

Usage

The docs folder contains the file user_guide.pdf with the instructions necessary to use the program. You can also display help by running the program with the -h option.

Publications

Journals

1. J.J. Escobar, J. Ortega, A.F. Díaz, J. González, and M. Damas. Energy-aware Load Balancing of Parallel Evolutionary Algorithms with Heavy Fitness Functions in Heterogeneous CPU-GPU Architectures. In: Concurrency and Computation: Practice and Experience 31.6 (2019), e4688. DOI: 10.1002/cpe.4688.
2. J.J. Escobar, J. Ortega, A.F. Díaz, J. González, and M. Damas. Time-energy Analysis of Multi-level Parallelism in Heterogeneous Clusters: The Case of EEG Classification in BCI Tasks. In: The Journal of Supercomputing 75.7 (2019), pp. 3397-3425. DOI: 10.1007/s11227-019-02908-4.
3. J.J. Escobar, J. Ortega, A.F. Díaz, J. González, and M. Damas. A Power-Performance Perspective to Multiobjective Electroencephalogram Feature Selection on Heterogeneous Parallel Platforms. In: Journal of Computational Biology 25.8 (2018), pp. 882-893. DOI: 10.1089/cmb.2018.0080.
4. J.J. Escobar, J. Ortega, J. González, M. Damas, and A.F. Díaz. Parallel High-dimensional Multiobjective Feature Selection for EEG Classification with Dynamic Workload Balancing on CPU-GPU. In: Cluster Computing 20.3 (2017), pp. 1881-1897. DOI: 10.1007/s10586-017-0980-7.

Conferences

1. J.J. Escobar, J. Ortega, A.F. Díaz, J. González, and M. Damas. A Parallel and Distributed Multi-population GA with Asynchronous Migrations: Energy-time Analysis for Heterogeneous Systems. In: Proceedings of the Annual Conference on Genetic and Evolutionary Computation. GECCO'2020. Cancun, Mexico: ACM, July 2020, pp. 211-212. DOI: 10.1145/3377929.3389908.
2. J.J. Escobar, J. Ortega, A.F. Díaz, J. González, and M. Damas. Energy-time Analysis of Heterogeneous Clusters for EEG Classification. In: Proceedings of the 3rd International Workshop on Power-Aware Computing. PACO'2019. Magdeburg, Germany: Zenodo, November 2019, pp. 7-9. DOI: 10.5281/zenodo.5572831.
3. J.J. Escobar, J. Ortega, A.F. Díaz, J. González, and M. Damas. Multi-objective Feature Selection for EEG Classification with Multi-Level Parallelism on Heterogeneous CPU-GPU Clusters. In: Proceedings of the Annual Conference on Genetic and Evolutionary Computation. GECCO'2018. Kyoto, Japan: ACM, July 2018, pp. 1862-1869. DOI: 10.1145/3205651.3208239.
4. J.J. Escobar, J. Ortega, A.F. Díaz, J. González, and M. Damas. Speedup and Energy Analysis of EEG Classification for BCI Tasks on CPU-GPU Clusters. In: Proceedings of the 6th International Workshop on Parallelism in Bioinformatics. PBIO'2018. Barcelona, Spain: ACM, September 2018, pp. 33-43. DOI: 10.1145/3235830.3235834.
5. J.J. Escobar, J. Ortega, A.F. Díaz, J. González, and M. Damas. Power-Performance Evaluation of Parallel Multi-objective EEG Feature Selection on CPU-GPU Platforms. In: Proceedings of the 5th International Workshop on Parallelism in Bioinformatics. PBIO'2017. Helsinki, Finland: Springer, August 2017, pp. 580-590. DOI: 10.1007/978-3-319-65482-9_43.
6. J.J. Escobar, J. Ortega, J. González, M. Damas, and B. Prieto. Issues on GPU Parallel Implementation of Evolutionary High-Dimensional Multi-objective Feature Selection. In: Proceedings of the 20th European Conference on Applications of Evolutionary Computation, Part I. EVOSTAR'2017. Amsterdam, The Netherlands: Springer, April 2017, pp. 773-788. DOI: 10.1007/978-3-319-55849-3_50.
7. J. Ortega, J.J. Escobar, A.F. Díaz, J. González, and M. Damas. Energy-aware Scheduling for Parallel Evolutionary Algorithms in Heterogeneous Architectures. In: Proceedings of the 2nd International Workshop on Power-Aware Computing. PACO'2017. Schloss Ringberg, Germany: Zenodo, July 2017, pp. 27-32. DOI: 10.5281/zenodo.814806.
8. J.J. Escobar, J. Ortega, J. González, and M. Damas. Assessing Parallel Heterogeneous Computer Architectures for Multiobjective Feature Selection on EEG Classification. In: Proceedings of the 4th International Conference on Bioinformatics and Biomedical Engineering. IWBBIO'2016. Granada, Spain: Springer, April 2016, pp. 277-289. DOI: 10.1007/978-3-319-31744-1_25.
9. J.J. Escobar, J. Ortega, J. González, and M. Damas. Improving Memory Accesses for Heterogeneous Parallel Multi-objective Feature Selection on EEG Classification. In: Proceedings of the 4th International Workshop on Parallelism in Bioinformatics. PBIO'2016. Grenoble, France: Springer, August 2016, pp. 372-383. DOI: 10.1007/978-3-319-58943-5_30.

Funding

This work has been funded by:

• Spanish Ministerio de Economía y Competitividad under grants number TIN2012-32039 and TIN2015-67020-P.
• Spanish Ministerio de Ciencia, Innovación y Universidades under grant number PGC2018-098813-B-C31.
• European Regional Development Fund (ERDF).

License

GNU GPLv3.

Copyright

Hpmoon © 2015 EFFICOMP.