Evaluation of Clustering Algorithms on GPU-Based Edge Computing Platforms

[EN] Internet of Things (IoT) is becoming a new socioeconomic revolution in which data and immediacy are the main ingredients. IoT generates large datasets on a daily basis but it is currently considered as "dark data", i.e., data generated but never analyzed. The efficient analysis of this data is mandatory to create intelligent applications for the next generation of IoT applications that benefits society. Artificial Intelligence (AI) techniques are very well suited to identifying hidden patterns and correlations in this data deluge. In particular, clustering algorithms are of the utmost importance for performing exploratory data analysis to identify a set (a.k.a., cluster) of similar objects. Clustering algorithms are computationally heavy workloads and require to be executed on high-performance computing clusters, especially to deal with large datasets. This execution on HPC infrastructures is an energy hungry procedure with additional issues, such as high-latency communications or privacy. Edge computing is a paradigm to enable light-weight computations at the edge of the network that has been proposed recently to solve these issues. In this paper, we provide an in-depth analysis of emergent edge computing architectures that include low-power Graphics Processing Units (GPUs) to speed-up these workloads. Our analysis includes performance and power consumption figures of the latest Nvidia's AGX Xavier to compare the energy-performance ratio of these low-cost platforms with a high-performance cloud-based counterpart version. Three different clustering algorithms (i.e., k-means, Fuzzy Minimals (FM), and Fuzzy C-Means (FCM)) are designed to be optimally executed on edge and cloud platforms, showing a speed-up factor of up to 11x for the GPU code compared to sequential counterpart versions in the edge platforms and energy savings of up to 150% between the edge computing and HPC platforms.This work has been partially supported by the Spanish Ministry of Science and Innovation, under the Ramon y Cajal Program (Grant No. RYC2018-025580-I) and under grants RTI2018-096384-B-I00, RTC-2017-6389-5 and RTC2019-007159-5 and by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18.Cecilia-Canales, JM.; Cano, J.; Morales-García, J.; Llanes, A.; Imbernón, B. (2020). Evaluation of Clustering Algorithms on GPU-Based Edge Computing Platforms. Sensors. 20(21):1-19. https://doi.org/10.3390/s20216335S1192021Gebauer, H., Fleisch, E., Lamprecht, C., & Wortmann, F. (2020). Growth paths for overcoming the digitalization paradox. Business Horizons, 63(3), 313-323. doi:10.1016/j.bushor.2020.01.005Guillén, M. A., Llanes, A., Imbernón, B., Martínez-España, R., Bueno-Crespo, A., Cano, J.-C., & Cecilia, J. M. (2020). Performance evaluation of edge-computing platforms for the prediction of low temperatures in agriculture using deep learning. The Journal of Supercomputing, 77(1), 818-840. doi:10.1007/s11227-020-03288-wWang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018). Deep learning for smart manufacturing: Methods and applications. Journal of Manufacturing Systems, 48, 144-156. doi:10.1016/j.jmsy.2018.01.003Gretzel, U., Sigala, M., Xiang, Z., & Koo, C. (2015). Smart tourism: foundations and developments. Electronic Markets, 25(3), 179-188. doi:10.1007/s12525-015-0196-8Pramanik, M. I., Lau, R. Y. K., Demirkan, H., & Azad, M. A. K. (2017). Smart health: Big data enabled health paradigm within smart cities. Expert Systems with Applications, 87, 370-383. doi:10.1016/j.eswa.2017.06.027Weber, M., & Podnar Žarko, I. (2019). A Regulatory View on Smart City Services. Sensors, 19(2), 415. doi:10.3390/s19020415Ghosh, A., Chakraborty, D., & Law, A. (2018). Artificial intelligence in Internet of things. CAAI Transactions on Intelligence Technology, 3(4), 208-218. doi:10.1049/trit.2018.1008Monti, L., Vincenzi, M., Mirri, S., Pau, G., & Salomoni, P. (2020). RaveGuard: A Noise Monitoring Platform Using Low-End Microphones and Machine Learning. Sensors, 20(19), 5583. doi:10.3390/s20195583Kumar, P., Sinha, K., Nere, N. K., Shin, Y., Ho, R., Mlinar, L. B., & Sheikh, A. Y. (2020). A machine learning framework for computationally expensive transient models. Scientific Reports, 10(1). doi:10.1038/s41598-020-67546-wMittal, S., & Vetter, J. S. (2015). A Survey of CPU-GPU Heterogeneous Computing Techniques. ACM Computing Surveys, 47(4), 1-35. doi:10.1145/2788396Singh, D., & Reddy, C. K. (2014). A survey on platforms for big data analytics. Journal of Big Data, 2(1). doi:10.1186/s40537-014-0008-6Khayyat, M., Elgendy, I. A., Muthanna, A., Alshahrani, A. S., Alharbi, S., & Koucheryavy, A. (2020). Advanced Deep Learning-Based Computational Offloading for Multilevel Vehicular Edge-Cloud Computing Networks. IEEE Access, 8, 137052-137062. doi:10.1109/access.2020.3011705Satyanarayanan, M. (2017). The Emergence of Edge Computing. Computer, 50(1), 30-39. doi:10.1109/mc.2017.9Capra, M., Peloso, R., Masera, G., Roch, M. R., & Martina, M. (2019). Edge Computing: A Survey On the Hardware Requirements in the Internet of Things World. Future Internet, 11(4), 100. doi:10.3390/fi11040100Lu, H., Gu, C., Luo, F., Ding, W., & Liu, X. (2020). Optimization of lightweight task offloading strategy for mobile edge computing based on deep reinforcement learning. Future Generation Computer Systems, 102, 847-861. doi:10.1016/j.future.2019.07.019Mimmack, G. M., Mason, S. J., & Galpin, J. S. (2001). Choice of Distance Matrices in Cluster Analysis: Defining Regions. Journal of Climate, 14(12), 2790-2797. doi:10.1175/1520-0442(2001)0142.0.co;2Gimenez, C. (2006). Logistics integration processes in the food industry. International Journal of Physical Distribution & Logistics Management, 36(3), 231-249. doi:10.1108/09600030610661813Chang, P.-C., Liu, C.-H., & Fan, C.-Y. (2009). Data clustering and fuzzy neural network for sales forecasting: A case study in printed circuit board industry. Knowledge-Based Systems, 22(5), 344-355. doi:10.1016/j.knosys.2009.02.005Zheng, B., Yoon, S. W., & Lam, S. S. (2014). Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Systems with Applications, 41(4), 1476-1482. doi:10.1016/j.eswa.2013.08.044Woodley, A., Tang, L.-X., Geva, S., Nayak, R., & Chappell, T. (2019). Parallel K-Tree: A multicore, multinode solution to extreme clustering. Future Generation Computer Systems, 99, 333-345. doi:10.1016/j.future.2018.09.038Kwedlo, W., & Czochanski, P. J. (2019). A Hybrid MPI/OpenMP Parallelization of $K$ -Means Algorithms Accelerated Using the Triangle Inequality. IEEE Access, 7, 42280-42297. doi:10.1109/access.2019.2907885Liu, B., He, S., He, D., Zhang, Y., & Guizani, M. (2019). A Spark-Based Parallel Fuzzy $c$ -Means Segmentation Algorithm for Agricultural Image Big Data. IEEE Access, 7, 42169-42180. doi:10.1109/access.2019.2907573Baydoun, M., Ghaziri, H., & Al-Husseini, M. (2018). CPU and GPU parallelized kernel K-means. The Journal of Supercomputing, 74(8), 3975-3998. doi:10.1007/s11227-018-2405-7Li, Y., Zhao, K., Chu, X., & Liu, J. (2013). Speeding up k-Means algorithm by GPUs. Journal of Computer and System Sciences, 79(2), 216-229. doi:10.1016/j.jcss.2012.05.004Cuomo, S., De Angelis, V., Farina, G., Marcellino, L., & Toraldo, G. (2019). A GPU-accelerated parallel K-means algorithm. Computers & Electrical Engineering, 75, 262-274. doi:10.1016/j.compeleceng.2017.12.002Al-Ayyoub, M., Abu-Dalo, A. M., Jararweh, Y., Jarrah, M., & Sa’d, M. A. (2015). A GPU-based implementations of the fuzzy C-means algorithms for medical image segmentation. The Journal of Supercomputing, 71(8), 3149-3162. doi:10.1007/s11227-015-1431-yAit Ali, N., Cherradi, B., El Abbassi, A., Bouattane, O., & Youssfi, M. (2018). GPU fuzzy c-means algorithm implementations: performance analysis on medical image segmentation. Multimedia Tools and Applications, 77(16), 21221-21243. doi:10.1007/s11042-017-5589-6Timón, I., Soto, J., Pérez-Sánchez, H., & Cecilia, J. M. (2016). Parallel implementation of fuzzy minimals clustering algorithm. Expert Systems with Applications, 48, 35-41. doi:10.1016/j.eswa.2015.11.011Cebrian, J. M., Imbernón, B., Soto, J., García, J. M., & Cecilia, J. M. (2020). High-throughput fuzzy clustering on heterogeneous architectures. Future Generation Computer Systems, 106, 401-411. doi:10.1016/j.future.2020.01.022Cecilia, J. M., Timon, I., Soto, J., Santa, J., Pereniguez, F., & Munoz, A. (2018). High-Throughput Infrastructure for Advanced ITS Services: A Case Study on Air Pollution Monitoring. IEEE Transactions on Intelligent Transportation Systems, 19(7), 2246-2257. doi:10.1109/tits.2018.2816741Sriramakrishnan, P., Kalaiselvi, T., & Rajeswaran, R. (2019). Modified local ternary patterns technique for brain tumour segmentation and volume estimation from MRI multi-sequence scans with GPU CUDA machine. Biocybernetics and Biomedical Engineering, 39(2), 470-487. doi:10.1016/j.bbe.2019.02.002Fang, Y., Chen, Q., & Xiong, N. (2019). A multi-factor monitoring fault tolerance model based on a GPU cluster for big data processing. Information Sciences, 496, 300-316. doi:10.1016/j.ins.2018.04.053Rodriguez, M. Z., Comin, C. H., Casanova, D., Bruno, O. M., Amancio, D. R., Costa, L. da F., & Rodrigues, F. A. (2019). Clustering algorithms: A comparative approach. PLOS ONE, 14(1), e0210236. doi:10.1371/journal.pone.0210236Pandove, D., Goel, S., & Rani, R. (2018). Systematic Review of Clustering High-Dimensional and Large Datasets. ACM Transactions on Knowledge Discovery from Data, 12(2), 1-68. doi:10.1145/3132088Bezdek, J. C., Ehrlich, R., & Full, W. (1984). FCM: The fuzzy c-means clustering algorithm. Computers & Geosciences, 10(2-3), 191-203. doi:10.1016/0098-3004(84)90020-7Soto, J., Flores-Sintas, A., & Palarea-Albaladejo, J. (2008). Improving probabilities in a fuzzy clustering partition. Fuzzy Sets and Systems, 159(4), 406-421. doi:10.1016/j.fss.2007.08.016Kolen, J. F., & Hutcheson, T. (2002). Reducing the time complexity of the fuzzy c-means algorithm. IEEE Transactions on Fuzzy Systems, 10(2), 263-267. doi:10.1109/91.99512

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