skip to main content
10.1145/3235830.3235834acmotherconferencesArticle/Chapter ViewAbstractPublication PagespbioConference Proceedingsconference-collections
research-article

Speedup and Energy Analysis of EEG Classification for BCI Tasks on CPU-GPU Clusters

Published: 23 September 2018 Publication History

Abstract

Many data mining applications on bioinformatics and bioengineering require solving problems with different profiles from the point of view of their implicit parallelism. In this context, heterogeneous architectures comprised by interconnected nodes with multiple multi-core microprocessors and accelerators, such as vector processors, Graphics Processing Units (GPUs), or Field-Programmable Gate Arrays would constitute suitable platforms that offer the possibility of not only to accelerate the running time of the applications, but also to optimize the energy consumption. In this paper, we analyze the speedups and energy consumption of a parallel multiobjective approach for feature selection and classification of electroencephalograms in Brain Computing Interface tasks, by considering different implementation alternatives in a heterogeneous CPU-GPU cluster. The procedure is able to take advantage of parallelism through message-passing among the CPU-GPU nodes of the cluster (through shared-memory and thread-level parallelism in the CPU cores, and data-level parallelism and thread-level parallelism in the GPU). The experimental results show high code accelerations and high energy-savings: running times between 1.4 and 5.3% of the sequential time and energy consumptions between 5.9 and 11.6% of the energy consumed by the sequential execution.

References

[1]
T. Allen and R. Ge. 2016. Characterizing Power and Performance of GPU Memory Access. In Proceedings of the 4th International Workshop on Energy Efficient Supercomputing (E2SC'2016). IEEE Press, Salt Lake City, Utah, USA, 46--53.
[2]
O. Arbelaitz, I. Gurrutxaga, J. Muguerza, J.M. Pérez, and I. Perona. 2013. An Extensive Comparative Study of Cluster Validity Indices. Pattern Recognition 46, 1 (2013), 243--256.
[3]
J. Asensio-Cubero, J.Q. Gan, and R. Palaniappan. 2013. Multiresolution Analysis over Simple Graphs for Brain Computer Interfaces. Journal of Neural Engineering 10, 4 (2013), 21--26.
[4]
R. Barik, N. Farooqui, B.T. Lewis, C. Hu, and T. Shpeisman. 2016. A black-box approach to energy-aware scheduling on integrated CPU-GPU systems. In Proceedings of the 2016 International Symposium on Code Generation and Optimization (CGO'2016). ACM, Barcelona, Spain, 70--81.
[5]
J.M. Cebrín, G.D. Guerrero, and J.M. Garcia. 2012. Energy Efficiency Analysis of GPUs. In Proceedings of the 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW'2012). IEEE, Shanghai, China, 1014--1022.
[6]
P. Collet. 2013. Why GPGPUs for Evolutionary Computation? In Massively Parallel Evolutionary Computation on GPGPUs, S. Tsutsui and P. Collet (Eds.). Springer, 3--14.
[7]
D. De Sensi. 2016. Predicting Performance and Power Consumption of Parallel Applications. In Proceedings of the 24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP'2016). IEEE, Heraklion Crete, Greece, 200--207.
[8]
J.J. Escobar, J. Ortega, A.F. Díaz, J. González, and M. Damas. 2017. Power-Performance Evaluation of Parallel Multiobjective EEG Feature Selection on CPU-GPU Platforms. In Proceedings of the 17th International Conference on Algorithms and Architectures for Parallel Processing (ICA3PP'2017). Springer, Helsinki, Finland, 580--590.
[9]
J.J. Escobar, J. Ortega, A.F. Díaz, J. González, and M. Damas. 2018. Energy-aware load balancing of parallel evolutionary algorithms with heavy fitness functions in heterogeneous CPU-GPU architectures. Concurrency and Computation: Practice and Experience (2018).
[10]
J.J. Escobar, J. Ortega, A.F. Díaz, J. González, and M. Damas. Not published, 2018. 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). ACM, Kyoto, Japan.
[11]
J.J. Escobar, J. Ortega, J. González, and M. Damas. 2016. 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), F. Ortuño and I. Rojas (Eds.). Springer, Granada, Spain, 277--289.
[12]
J.J. Escobar, J. Ortega, J. González, and M. Damas. 2016. Improving Memory Accesses for Heterogeneous Parallel Multiobjective Feature Selection on EEG Classification. In Proceedings of the 4th International Workshop on Parallelism in Bioinformatics (PBIO'2016). Springer, Grenoble, France, 372--383.
[13]
J.J. Escobar, J. Ortega, J. González, M. Damas, and A.F. Díaz. 2017. Parallel high-dimensional multiobjective feature selection for EEG classification with dynamic workload balancing on CPU-GPU. Cluster Computing 20, 3 (2017), 1881--1897.
[14]
F. Fernández-de-Vega, F. Chávez, J. Díaz, J.A. García, P.A. Castillo, J.J. Merelo, and C. Cotta. 2016. A Cross-Platform Assessment of Energy Consumption in Evolutionary Algorithms. In Proceedings of the 14th International Conference on Parallel Problem Solving from Nature (PPSN 2016). Springer, Edinburgh, UK, 548--557.
[15]
A. Gainaru, E. Slusanschi, and S. Trausan-Matu. 2011. Mapping Data Mining Algorithms on a GPU Architecture: A Study. In Proceedings of the 19th International Symposium. Foundations of Intelligent Systems (ISMIS '2011), M. Kryszkiewicz, H. Rybinski, A. Skowron, and Z-W. Raś (Eds.). Springer, Warsaw, Poland, 102--112.
[16]
E.M. Garzón, J.J Moreno, and J.A. Martínez. 2017. An approach to optimise the energy efficiency of iterative computation on integrated GPU--CPU systems. The Journal of Supercomputing 73, 1 (2017), 114--125.
[17]
Y.J. Gong, W.N. Chen, Z.H. Zhan, J. Zhang, Y. Li, Q. Zhang, and J.J. Li. 2015. Distributed evolutionary algorithms and their models: A survey of the state-of-the-art. Applied Soft Computing 34, C (2015), 286--300.
[18]
J. Handl and J. Knowles. 2006. Feature Subset Selection in Unsupervised Learning via Multiobjective Optimization. International Journal of Computational Intelligence Research 2, 3 (2006), 217--238.
[19]
Khronos Group. 2015. Khronos OpenCL Registry. https://www.khronos.org/registry/cl/. Accessed: 2015-11--30.
[20]
T.V. Luong, N. Melab, and E-G. Talbi. 2010. GPU-based Island Model for Evolutionary Algorithms. In Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation (GECCO'2010). ACM, Portland, OR, USA, 1089--1096.
[21]
A. Marowka. 2012. Energy Consumption Modeling for Hybrid Computing. In Proceedings of the 18th International Conference on Parallel Processing, Euro-Par 2012 (Euro-Par'2012). Springer, Rhodes Island, Greece, 54--64.
[22]
S. Mittal and J.S Vetter. 2014. A Survey of Methods for Analyzing and Improving GPU Energy Efficiency. Comput. Surveys 47, 2 (2014), 19:1--19:23.
[23]
S. Mittal and J.S Vetter. 2015. A Survey of CPU-GPU Heterogeneous Computing Techniques. Comput. Surveys 47, 4 (2015), 69:1--69:35.
[24]
A. Mukhopadhyay, U. Maulik, S. Bandyopadhyay, and C.A. Coello Coello. 2014. A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part I. IEEE Transactions on Evolutionary Computation 18, 1 (2014), 4--19.
[25]
A. Mukhopadhyay, U. Maulik, S. Bandyopadhyay, and C.A. Coello Coello. 2014. A Survey of Multiobjective Evolutionary Algorithms for Data Mining: Part II. IEEE Transactions on Evolutionary Computation 18, 1 (2014), 20--35.
[26]
K. O'brien, I. Pietri, R. Reddy, A. Lastovetsky, and R. Sakellariou. 2017. A Survey of Power and Energy Predictive Models in HPC Systems and Applications. Comput. Surveys 50, 3 (2017), 37:1--37:38.
[27]
OpenMP Community. Accessed: 2016-11-21. OpenMP specifications. http://www.openmp.org/specifications/.
[28]
J. Ortega, J. Asensio-Cubero, J.Q. Gan, and A. Ortiz. 2016. Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection. BioMedical Engineering OnLine 15, 1 (2016), 73.
[29]
B. Pérez, E. Stafford J.L Bosque, and R. Beivide. 2017. Energy efficiency of load balancing for data-parallel applications in heterogeneous systems. The Journal of Supercomputing 73, 1 (2017), 330--342.
[30]
P. Pospichal, J. Jaros, and J. Schwarz. 2010. Parallel Genetic Algorithm on the CUDA Architecture. In Proceedings of the 13th European Conference on the Applications of Evolutionary Computation (EvoApplications'2010), C. Di Chio, S. Cagnoni, C. Cotta, M. Ebner, A. Ekárt, A.I. Esparcia-Alcazar, C-K. Goh, J.J. Merelo, F. Neri, M. PreuSS, J. Togelius, and G.N. Yannakakis (Eds.). Springer, Istambul, Turkey, 442--451.
[31]
D. Sharma and P. Collet. 2013. Implementation Techniques for Massively Parallel Multi-objective Optimization. In Massively Parallel Evolutionary Computation on GPGPUs, S. Tsutsui and P. Collet (Eds.). Springer, 267--286.
[32]
P. Vidal, E. Alba, and F. Luna. 2017. Solving Optimization Problems Using a Hybrid Systolic Search on GPU Plus CPU. Soft Computing 21, 12 (2017), 3227--3245.
[33]
Y. Wang and N. Ranganathan. 2011. An Instruction-Level Energy Estimation and Optimization Methodology for GPU. In Proceedings of the 11th International Conference on Computer and Information Technology (CIT'2011). IEEE, Paphos, Cyprus, 621--628.
[34]
M.L. Wong and G. Cui. 2013. Data Mining Using Parallel Multi-objective Evolutionary Algorithms on Graphics Processing Units. In Massively Parallel Evolutionary Computation on GPGPUs, S. Tsutsui and P. Collet (Eds.). Springer, 287--307.
[35]
E. Zitzler. 1999. Evolutionary Algorithms for Multiobjective Optimization: Methods and Applications. Shaker Verlag Germany.
[36]
E. Zitzler and L. Thiele. 1998. Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study. In Proceedings of the 5th International Conference on Parallel Problem Solving from Nature (PPSN V). Springer, Amsterdam, The Netherlands, 292--301.

Cited By

View all
  • (2019)Time-energy analysis of multilevel parallelism in heterogeneous clustersThe Journal of Supercomputing10.1007/s11227-019-02908-475:7(3397-3425)Online publication date: 1-Jul-2019

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
PBio 2018: Proceedings of the 6th International Workshop on Parallelism in Bioinformatics
September 2018
70 pages
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 September 2018

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. BCI Tasks
  2. Distributed Programming
  3. EEG Classification
  4. Energy-aware Computing
  5. Heterogeneous Cluster
  6. Parallelism

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

PBio 2018

Acceptance Rates

PBio 2018 Paper Acceptance Rate 7 of 9 submissions, 78%;
Overall Acceptance Rate 7 of 9 submissions, 78%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)9
  • Downloads (Last 6 weeks)3
Reflects downloads up to 25 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2019)Time-energy analysis of multilevel parallelism in heterogeneous clustersThe Journal of Supercomputing10.1007/s11227-019-02908-475:7(3397-3425)Online publication date: 1-Jul-2019

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media