CONVERGENCE OF HPC AND AI: TWO DIRECTIONS OF CONNECTION

Details

Hits: 2898

Volume 1 (2), December 2018, Pages 179-184

Nigar Ismayilova, Elviz Ismayilov

Department of General and Applied Mathematics, HPC Advance Research Center, Azerbaijan State Oil and Industry University, This email address is being protected from spambots. You need JavaScript enabled to view it., This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

This paper examines the role of HPC systems in the solution of the most AI problems, on the other hand, assesses the impact of the application of AI methods on the resolution of different tasks in distributed systems. The findings from the literature review illustrate how these two main fields of science and information technologies can be converged together for the achievement of the goals in designing and developing of the intelligent agents with the high level of intelligence, also for the solution of optimization problems in distributed systems with the increasing complexity.

Keywords:

HPC, AI, fuzzy load balancing, exascale load balancing, predicting HPC.

DOI: https://doi.org/10.32010/26166127.2018.1.2.179.184

Reference 

[1] Sammut, C., & Webb, G. I. (Eds.). (2011). Encyclopedia of machine learning. Springer Science & Business Media.

[2] Ganapathi, A., Datta, K., Fox, A., & Patterson, D. (2009, March). A case for machine learning to optimize multicore performance. In Proceedings of the First USENIX conference on Hot topics in parallelism (pp. 1-1). Berkeley, CA: USENIX Association.

[3] Chien, S. W. D., Sishtla, C. P., Markidis, S., Zhang, J., Peng, I. B., & Laure, E. (2018). An Evaluation of the TensorFlow Programming Model for Solving Traditional HPC Problems. In International Conference on Exascale Applications and Software (p. 34). The University of Edinburgh.

[4] Pittino, F., Diversi, R., Benini, L., & Bartolini, A. (2018). Robust online identification of thermal models for in-production HPC clusters with machine learning-based data selection. arXiv preprint arXiv:1810.01865.

[5] Hamada, S., Akiyama, S., & Namiki, M. (2018). Reactive NaN Repair for Applying Approximate Memory to Numerical Applications. arXiv preprint arXiv:1804.00705.

[6] Berral, J. L., Goiri, Í., Nou, R., Julià, F., Guitart, J., Gavaldà, R., & Torres, J. (2010, April). Towards energy-aware scheduling in data centers using machine learning. In Proceedings of the 1st International Conference on energy-Efficient Computing and Networking (pp. 215-224). ACM.

[7] Amodei, D., Ananthanarayanan, S., Anubhai, R., Bai, J., Battenberg, E., Case, C., ... & Chen, J. (2016, June). Deep speech 2: End-to-end speech recognition in English and mandarin. In International Conference on Machine Learning (pp. 173-182)

[8] Cireşan, D., Meier, U., & Schmidhuber, J. (2012). Multi-column deep neural networks for image classification. arXiv preprint arXiv:1202.2745.

[7] Esmaeilzadeh, H., Sampson, A., Ceze, L., & Burger, D. (2012, December). Neural acceleration for general-purpose approximate programs. In Proceedings of the 2012 45th Annual IEEE/ACM International Symposium on Microarchitecture (pp. 449-460). IEEE Computer Society.

[8] Temam, O. (2012). A defect-tolerant accelerator for emerging high-performance applications. ACM SIGARCH Computer Architecture News, 40(3), 356-367.

[9] Boehm, M., Tatikonda, S., Reinwald, B., Sen, P., Tian, Y., Burdick, D. R., & Vaithyanathan, S. (2014). Hybrid parallelization strategies for large-scale machine learning in SystemML. Proceedings of the VLDB Endowment, 7(7), 553-564.

[10] Coates, A., Huval, B., Wang, T., Wu, D., Catanzaro, B., & Andrew, N. (2013, February). Deep learning with COTS HPC systems. In International Conference on Machine Learning (pp. 1337-1345).

[11] Gaussier, E., Glesser, D., Reis, V., & Trystram, D. (2015, November). Improving backfilling by using machine learning to predict running times. In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (p. 64). ACM.

[12] Elsebakhi, E., Lee, F., Schendel, E., Haque, A., Kathireason, N., Pathare, T., ... & Al-Ali, R. (2015). Large-scale machine learning based on functional networks for biomedical big data with high performance computing platforms. Journal of Computational Science, 11, 69-81.

[13] Suthaharan, S. (2014). Big data classification: Problems and challenges in network intrusion prediction with machine learning. ACM SIGMETRICS Performance Evaluation Review, 41(4), 70-73.

[14] Grimmer, J. (2015). We are all social scientists now: how big data, machine learning, and causal inference work together. PS: Political Science & Politics, 48(1), 80-83.

[15] Landset, S., Khoshgoftaar, T. M., Richter, A. N., & Hasanin, T. (2015). A survey of open source tools for machine learning with big data in the Hadoop ecosystem. Journal of Big Data, 2(1), 24.

[16] Madden, S. (2012). From databases to big data. IEEE Internet Computing, (3), 4-6.

[17] Sim, K. M., & Sun, W. H. (2003). Ant colony optimization for routing and load-balancing: survey and new directions. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 33(5), 560-572.

[18] Dasgupta, K., Mandal, B., Dutta, P., Mandal, J. K., & Dam, S. (2013). A genetic algorithm (ga) based load balancing strategy for cloud computing. Procedia Technology, 10, 340-347.

[19] Dam, S., Mandal, G., Dasgupta, K., & Dutta, P. (2015, February). Genetic algorithm and gravitational emulation based hybrid load balancing strategy in cloud computing. In 2015 Third International Conference on Computer, Communication, Control and Information Technology (C3IT) (pp. 1-7). IEEE.

[20] Sigal, L., & Glauberman, A. (2012). U.S. Patent No. 8,185,909. Washington, DC: U.S. Patent and Trademark Office.

[21] Prevost, J. J., Nagothu, K., Kelley, B., & Jamshidi, M. (2011, June). Prediction of cloud data center networks loads using stochastic and neural models. In System of Systems Engineering (SoSE), 2011 6th International Conference on (pp. 276-281). IEEE.

[22] Geist, A., & Lucas, R. (2009). Major computer science challenges at exascale. The International Journal of High Performance Computing Applications, 23(4), 427-436.

[23] Ramyani Saleh, S., Mousavi Khaneghah, E., Shadnoush, N., & Aliev, A. R. (2018). A mathematical framework for managing interactive communication distortions in exascale organizations. Cogent Business & Management, 5: 1545356, 1-23.