Re-engineering the ant colony optimization for CMP architectures

[EN] The ant colony optimization (ACO) is inspired by the behavior of real ants, and as a bioinspired method, its underlying computation is massively parallel by definition. This paper shows re-engineering strategies to migrate the ACO algorithm applied to the Traveling Salesman Problem to modern Intel-based multi- and many-core architectures in a step-by-step methodology. The paper provides detailed guidelines on how to optimize the algorithm for the intra-node (thread and vector) parallelization, showing the performance scalability along with the number of cores on different Intel architectures, reporting up to 5.5x speedup factor between the Intel Xeon Phi Knights Landing and Intel Xeon v2. Moreover, parallel efficiency is provided for all targeted architectures, finding that core load imbalance, memory bandwidth limitations, and NUMA effects on data placement are some of the key factors limiting performance. Finally, a distributed implementation is also presented, reaching up to 2.96x speedup factor when running the code on 3 nodes over the single-node counterpart version. In the latter case, the parallel efficiency is affected by the synchronization frequency, which also affects the quality of the solution found by the distributed implementation.This work was partially supported by the Fundación Séneca, Agencia de Ciencia y Tecnología de la Región de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities as well as European Commission FEDER funds under Grants TIN2015-66972-C5-3-R, RTI2018-098156-B-C53, TIN2016-78799-P (AEI/FEDER, UE), and RTC-2017-6389-5. We acknowledge the excellent work done by Victor Montesinos while he was doing a research internship supported by the University of Murcia.Cecilia-Canales, JM.; García Carrasco, JM. (2020). Re-engineering the ant colony optimization for CMP architectures. The Journal of Supercomputing (Online). 76(6):4581-4602. https://doi.org/10.1007/s11227-019-02869-8S45814602766Yang XS (2010) Nature-inspired metaheuristic algorithms. Luniver Press, LebanonAkila M, Anusha P, Sindhu M, Selvan Krishnasamy T (2017) Examination of PSO, GA-PSO and ACO algorithms for the design optimization of printed antennas. In: IEEE Applied Electromagnetics Conference (AEMC)Dorigo M, Stützle T (2004) Ant colony optimization. A bradford book. The MIT Press, CambridgeCecilia JM, García JM, Nisbet A, Amos M, Ujaldón M (2013) Enhancing data parallelism for ant colony optimization on GPUs. J Parallel Distrib Comput 73(1):42–51Dawson L, Stewart I (2013) Improving ant colony optimization performance on the GPU using CUDA. In: IEEE Conference on Evolutionary Computation, pp 1901–1908Llanes A, Cecilia JM, Sánchez A, García JM, Amos M, Ujaldón M (2016) Dynamic load balancing on heterogeneous clusters for parallel ant colony optimization. Cluster Comput 19(1):1–11Cecilia JM, Llanes A, Abellán JL, Gómez-Luna J, Chang L, Hwu WW (2018) High-throughput ant colony optimization on graphics processing units. J Parallel Distrib Comput 113:261–274Lloyd H, Amos M (2016) A Highly Parallelized and Vectorized Implementation of Max–Min Ant System on Intel Xeon Phi. In: IEEE computational intelligenceTirado F, Barrientos RJ, González P, Mora M (2017) Efficient exploitation of the Xeon Phi architecture for the ant colony optimization (ACO) metaheuristic. J Supercomput 73(11):5053–5070Montesinos V, García JM (2018) Vectorization strategies for ant colony optimization on intel architectures. Parallel Computing is Everywhere. IOS Press, Amsterdam, pp 400–409Lawler E, Lenstra J, Kan A, Shmoys D (1987) The Traveling salesman problem. Wiley, New YorkMontesinos V (June 2018) Performance analysis of ant colony optimization on intel architectures. 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Offloading strategies for Stencil kernels on the KNC Xeon Phi architecture: Accuracy versus performance

[EN] The ever-increasing computational requirements of HPC and service provider applications are becoming a great challenge for hardware and software designers. These requirements are reaching levels where the isolated development on either computational field is not enough to deal with such challenge. A holistic view of the computational thinking is therefore the only way to success in real scenarios. However, this is not a trivial task as it requires, among others, of hardware¿software codesign. In the hardware side, most high-throughput computers are designed aiming for heterogeneity, where accelerators (e.g. Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), etc.) are connected through high-bandwidth bus, such as PCI-Express, to the host CPUs. Applications, either via programmers, compilers, or runtime, should orchestrate data movement, synchronization, and so on among devices with different compute and memory capabilities. This increases the programming complexity and it may reduce the overall application performance. This article evaluates different offloading strategies to leverage heterogeneous systems, based on several cards with the firstgeneration Xeon Phi coprocessors (Knights Corner). We use a 11-point 3-D Stencil kernel that models heat dissipation as a case study. Our results reveal substantial performance improvements when using several accelerator cards. Additionally, we show that computing of an approximate result by reducing the communication overhead can yield 23% performance gains for double-precision data sets.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work is jointly supported by the Fundacion Seneca (Agencia Regional de Ciencia y Tecnologia, Region de Murcia) under grants 15290/PI/2010 and 18946/JLI/13 and by the Spanish MINECO, as well as European Commission FEDER funds, under grants TIN2015-66972-C5-3-R and TIN2016-78799-P (AEI/ FEDER, UE). MH was supported by a research grant from the PRODEP under the Professional Development Program for Teachers (UAGro-197) MéxicoHernández, M.; Cebrián, JM.; Cecilia-Canales, JM.; García, JM. (2020). Offloading strategies for Stencil kernels on the KNC Xeon Phi architecture: Accuracy versus performance. International Journal of High Performance Computing Applications. 34(2):199-297. https://doi.org/10.1177/1094342017738352S199297342Michael Brown, W., Carrillo, J.-M. Y., Gavhane, N., Thakkar, F. M., & Plimpton, S. J. (2015). Optimizing legacy molecular dynamics software with directive-based offload. Computer Physics Communications, 195, 95-101. doi:10.1016/j.cpc.2015.05.004Esmaeilzadeh, H., Blem, E., St. Amant, R., Sankaralingam, K., & Burger, D. (2012). Power Limitations and Dark Silicon Challenge the Future of Multicore. ACM Transactions on Computer Systems, 30(3), 1-27. doi:10.1145/2324876.2324879Feng, L. (2015). Data Transfer Using the Intel COI Library. High Performance Parallelism Pearls, 341-348. doi:10.1016/b978-0-12-802118-7.00020-0Jeffers, J., & Reinders, J. (2013). Offload. Intel Xeon Phi Coprocessor High Performance Programming, 189-241. doi:10.1016/b978-0-12-410414-3.00007-4Rahman, R. (2013). Intel® Xeon Phi™ Coprocessor Architecture and Tools. doi:10.1007/978-1-4302-5927-5Reinders J, Jeffers J (2014) High Performance Parallelism Pearls, Multicore and Many-core Programming Approaches (Characterization and Auto-tuning of 3DFD). Morgan Kaufmann, pp. 377–396.Shareef, B., de Doncker, E., & Kapenga, J. (2015). Monte Carlo simulations on Intel Xeon Phi: Offload and native mode. 2015 IEEE High Performance Extreme Computing Conference (HPEC). doi:10.1109/hpec.2015.7322456Ujaldón, M. (2016). CUDA Achievements and GPU Challenges Ahead. Lecture Notes in Computer Science, 207-217. doi:10.1007/978-3-319-41778-3_20Wang, E., Zhang, Q., Shen, B., Zhang, G., Lu, X., Wu, Q., & Wang, Y. (2014). High-Performance Computing on the Intel® Xeon Phi™. doi:10.1007/978-3-319-06486-4Wende, F., Klemm, M., Steinke, T., & Reinefeld, A. (2015). Concurrent Kernel Offloading. High Performance Parallelism Pearls, 201-223. doi:10.1016/b978-0-12-802118-7.00012-

Parallel implementation of fuzzy minimals clustering algorithm

Clustering aims to classify different patterns into groups called clusters. Many algorithms for both hard and fuzzy clustering have been developed to deal with exploratory data analysis in many contexts such as image processing, pattern recognition, etc. However, we are witnessing the era of big data computing where computing resources are becoming the main bottleneck to deal with those large datasets. In this context, sequential algorithms need to be redesigned and even rethought to fully leverage the emergent massively parallel architectures. In this paper, we propose a parallel implementation of the fuzzy minimals clustering algorithm called Parallel Fuzzy Minimal (PFM). Our experimental results reveal linear speed-up of PFM when compared to the sequential counterpart version, keeping very good classification quality.Ingeniería, Industria y Construcció

Evaluation of Clustering Algorithms on HPC Platforms

[EN] Clustering algorithms are one of the most widely used kernels to generate knowledge from large datasets. These algorithms group a set of data elements (i.e., images, points, patterns, etc.) into clusters to identify patterns or common features of a sample. However, these algorithms are very computationally expensive as they often involve the computation of expensive fitness functions that must be evaluated for all points in the dataset. This computational cost is even higher for fuzzy methods, where each data point may belong to more than one cluster. In this paper, we evaluate different parallelisation strategies on different heterogeneous platforms for fuzzy clustering algorithms typically used in the state-of-the-art such as the Fuzzy C-means (FCM), the Gustafson-Kessel FCM (GK-FCM) and the Fuzzy Minimals (FM). The experimental evaluation includes performance and energy trade-offs. Our results show that depending on the computational pattern of each algorithm, their mathematical foundation and the amount of data to be processed, each algorithm performs better on a different platform.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 by the Spanish "Agencia Estatal de Investigacion" under grant PID2020-112827GB-I00 /AEI/ 10.13039/501100011033, and under grants RTI2018-096384-B-I00, RTC-2017-6389-5 and RTC2019-007159-5, by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by the "Conselleria de Educacion, Investigacion, Cultura y Deporte, Direccio General de Ciencia i Investigacio, Proyectos AICO/2020", Spain, under Grant AICO/2020/302.Cebrian, JM.; Imbernón, B.; Soto, J.; Cecilia-Canales, JM. (2021). Evaluation of Clustering Algorithms on HPC Platforms. Mathematics. 9(17):1-20. https://doi.org/10.3390/math917215612091

High-throughput fuzzy clustering on heterogeneous architectures

[EN] The Internet of Things (IoT) is pushing the next economic revolution in which the main players are data and immediacy. IoT is increasingly producing large amounts of data that are now classified as "dark data'' because most are created but never analyzed. The efficient analysis of this data deluge is becoming mandatory in order to transform it into meaningful information. Among the techniques available for this purpose, clustering techniques, which classify different patterns into groups, have proven to be very useful for obtaining knowledge from the data. However, clustering algorithms are computationally hard, especially when it comes to large data sets and, therefore, they require the most powerful computing platforms on the market. In this paper, we investigate coarse and fine grain parallelization strategies in Intel and Nvidia architectures of fuzzy minimals (FM) algorithm; a fuzzy clustering technique that has shown very good results in the literature. We provide an in-depth performance analysis of the FM's main bottlenecks, reporting a speed-up factor of up to 40x compared to the sequential counterpart version.This work was partially supported by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities under grants TIN2016-78799-P (AEI/FEDER, UE), RTI2018-096384-B-I00, RTI2018-098156-B-C53 and RTC-2017-6389-5.Cebrian, JM.; Imbernón, B.; Soto, J.; García, JM.; Cecilia-Canales, JM. (2020). High-throughput fuzzy clustering on heterogeneous architectures. Future Generation Computer Systems. 106:401-411. https://doi.org/10.1016/j.future.2020.01.022S401411106Waldrop, M. M. (2016). The chips are down for Moore’s law. Nature, 530(7589), 144-147. doi:10.1038/530144aCecilia, 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.2018.2816741Singh, D., & Reddy, C. K. (2014). A survey on platforms for big data analytics. Journal of Big Data, 2(1). doi:10.1186/s40537-014-0008-6Stephens, N., Biles, S., Boettcher, M., Eapen, J., Eyole, M., Gabrielli, G., … Walker, P. (2017). The ARM Scalable Vector Extension. IEEE Micro, 37(2), 26-39. doi:10.1109/mm.2017.35Wright, S. A. (2019). Performance Modeling, Benchmarking and Simulation of High Performance Computing Systems. Future Generation Computer Systems, 92, 900-902. doi:10.1016/j.future.2018.11.020Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering. ACM Computing Surveys, 31(3), 264-323. doi:10.1145/331499.331504Lee, J., Hong, B., Jung, S., & Chang, V. (2018). Clustering learning model of CCTV image pattern for producing road hazard meteorological information. Future Generation Computer Systems, 86, 1338-1350. doi:10.1016/j.future.2018.03.022Pérez-Garrido, A., Girón-Rodríguez, F., Bueno-Crespo, A., Soto, J., Pérez-Sánchez, H., & Helguera, A. M. (2017). Fuzzy clustering as rational partition method for QSAR. Chemometrics and Intelligent Laboratory Systems, 166, 1-6. doi:10.1016/j.chemolab.2017.04.006H.S. Nagesh, S. Goil, A. Choudhary, A scalable parallel subspace clustering algorithm for massive data sets, in: Proceedings 2000 International Conference on Parallel Processing, 2000, pp. 477–484.Bezdek, 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)90020-7Havens, T. C., Bezdek, J. C., Leckie, C., Hall, L. O., & Palaniswami, M. (2012). Fuzzy c-Means Algorithms for Very Large Data. IEEE Transactions on Fuzzy Systems, 20(6), 1130-1146. doi:10.1109/tfuzz.2012.2201485Flores-Sintas, A., Cadenas, J., & Martin, F. (1998). A local geometrical properties application to fuzzy clustering. Fuzzy Sets and Systems, 100(1-3), 245-256. doi:10.1016/s0165-0114(97)00038-9Soto, 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.2007.08.016Timón, 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.2015.11.011Flores-Sintas, A., M. Cadenas, J., & Martin, F. (2001). Detecting homogeneous groups in clustering using the Euclidean distance. Fuzzy Sets and Systems, 120(2), 213-225. doi:10.1016/s0165-0114(99)00110-4Wang, H., Potluri, S., Luo, M., Singh, A. K., Sur, S., & Panda, D. K. (2011). MVAPICH2-GPU: optimized GPU to GPU communication for InfiniBand clusters. Computer Science - Research and Development, 26(3-4), 257-266. doi:10.1007/s00450-011-0171-3Kaltofen, E., & Villard, G. (2005). On the complexity of computing determinants. computational complexity, 13(3-4), 91-130. doi:10.1007/s00037-004-0185-3Johnson, S. C. (1967). Hierarchical clustering schemes. Psychometrika, 32(3), 241-254. doi:10.1007/bf02289588Saxena, A., Prasad, M., Gupta, A., Bharill, N., Patel, O. P., Tiwari, A., … Lin, C.-T. (2017). A review of clustering techniques and developments. Neurocomputing, 267, 664-681. doi:10.1016/j.neucom.2017.06.053Woodley, 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.2019.2907885Li, 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.2012.05.004Saveetha, V., & Sophia, S. (2018). Optimal Tabu K-Means Clustering Using Massively Parallel Architecture. Journal of Circuits, Systems and Computers, 27(13), 1850199. doi:10.1142/s0218126618501992Djenouri, Y., Djenouri, D., Belhadi, A., & Cano, A. (2019). Exploiting GPU and cluster parallelism in single scan frequent itemset mining. Information Sciences, 496, 363-377. doi:10.1016/j.ins.2018.07.020Krawczyk, B. (2016). GPU-Accelerated Extreme Learning Machines for Imbalanced Data Streams with Concept Drift. Procedia Computer Science, 80, 1692-1701. doi:10.1016/j.procs.2016.05.509Fang, 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.2018.04.053Tanweer, S., & Rao, N. (2019). Novel Algorithm of CPU-GPU hybrid system for health care data classification. Journal of Drug Delivery and Therapeutics, 9(1-s), 355-357. doi:10.22270/jddt.v9i1-s.244

Towards Energy Efficiency in Heterogeneous Processors: Findings on Virtual Screening Methods

The integration of the latest breakthroughs in computational modeling and high performance computing (HPC) has leveraged advances in the fields of healthcare and drug discovery, among others. By integrating all these developments together, scientists are creating new exciting personal therapeutic strategies for living longer that were unimaginable not that long ago. However, we are witnessing the biggest revolution in HPC in the last decade. Several graphics processing unit architectures have established their niche in the HPC arena but at the expense of an excessive power and heat. A solution for this important problem is based on heterogeneity. In this paper, we analyze power consumption on heterogeneous systems, benchmarking a bioinformatics kernel within the framework of virtual screening methods. Cores and frequencies are tuned to further improve the performance or energy efficiency on those architectures. Our experimental results show that targeted low‐cost systems are the lowest power consumption platforms, although the most energy efficient platform and the best suited for performance improvement is the Kepler GK110 graphics processing unit from Nvidia by using compute unified device architecture. Finally, the open computing language version of virtual screening shows a remarkable performance penalty compared with its compute unified device architecture counterpart.Ingeniería, Industria y Construcció

METADOCK: A parallel metaheuristic schema for virtual screening methods

Virtual screening through molecular docking can be translated into an optimization problem, which can be tackled with metaheuristic methods. The interaction between two chemical compounds (typically a protein, enzyme or receptor, and a small molecule, or ligand) is calculated by using highly computationally demanding scoring functions that are computed at several binding spots located throughout the protein surface. This paper introduces METADOCK, a novel molecular docking methodology based on parameterized and parallel metaheuristics and designed to leverage heterogeneous computers based on heterogeneous architectures. The application decides the optimization technique at running time by setting a configuration schema. Our proposed solution finds a good workload balance via dynamic assignment of jobs to heterogeneous resources which perform independent metaheuristic executions when computing different molecular interactions required by the scoring functions in use. A cooperative scheduling of jobs optimizes the quality of the solution and the overall performance of the simulation, so opening a new path for further developments of virtual screening methods on high-performance contemporary heterogeneous platforms.Ingeniería, Industria y Construcció

A high-performance IoT solution to reduce frost damages in stone fruits

[EN] Agriculture is one of the key sectors where technology is opening new opportunities to break up the market. The Internet of Things (IoT) could reduce the production costs and increase the product quality by providing intelligence services via IoT analytics. However, the hard weather conditions and the lack of connectivity in this field limit the successful deployment of such services as they require both, ie, fully connected infrastructures and highly computational resources. Edge computing has emerged as a solution to bring computing power in close proximity to the sensors, providing energy savings, highly responsive web services, and the ability to mask transient cloud outages. In this paper, we propose an IoT monitoring system to activate anti-frost techniques to avoid crop loss, by defining two intelligent services to detect outliers caused by the sensor errors. The former is a nearest neighbor technique and the latter is the k-means algorithm, which provides better quality results but it increases the computational cost. Cloud versus edge computing approaches are analyzed by targeting two different low-power GPUs. Our experimental results show that cloud-based approaches provides highest performance in general but edge computing is a compelling alternative to mask transient cloud outages and provide highly responsive data analytic services in technologically hostile environments.This work was partially supported by the Fundación Séneca del Centro de Coordinación de la Investigación de la Región de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities under grants TIN2016-78799-P (AEI/FEDER, UE) and RTC-2017-6389-5. Finally, we thank the farmers for the availability of their resources to be able to asses and improve the IoT monitoring system proposed.Guillén-Navarro, MA.; Martínez-España, R.; López, B.; Cecilia-Canales, JM. (2021). A high-performance IoT solution to reduce frost damages in stone fruits. Concurrency and Computation: Practice and Experience (Online). 33(2):1-14. https://doi.org/10.1002/cpe.529911433

Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain)

[EN] The Mar Menor is a hypersaline coastal lagoon with high environmental value and a characteristic example of a highly anthropized hydro-ecosystem located in the southeast of Spain. An unprecedented eutrophication crisis in 2016 and 2019 with abrupt changes in the quality of its waters caused a great social alarm. Understanding and modeling the level of a eutrophication indicator, such as chlorophyll-a (Chl-a), benefits the management of this complex system. In this study, we investigate the potential machine learning (ML) methods to predict the level of Chl-a. Particularly, Multilayer Neural Networks (MLNNs) and Support Vector Regressions (SVRs) are evaluated using as a target dataset information of up to nine different water quality parameters. The most relevant input combinations were extracted using wrapper feature selection methods which simplified the structure of the model, resulting in a more accurate and efficient procedure. Although the performance in the validation phase showed that SVR models obtained better results than MLNNs, experimental results indicated that both ML algorithms provide satisfactory results in the prediction of Chl-a concentration, reaching up to 0.7 R-CV(2) (cross-validated coefficient of determination) for the best-fit models.This research was partially funded by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by Spanish Ministry of Science, Innovation and Universities under grants RTI2018-096384-B-I00 and RTC-2017-6389-5.Jimeno-Sáez, P.; Senent-Aparicio, J.; Cecilia-Canales, JM.; Pérez-Sánchez, J. (2020). Using Machine-Learning Algorithms for Eutrophication Modeling: Case Study of Mar Menor Lagoon (Spain). International Journal of Environmental research and Public Health (Online). 17(4):1-14. https://doi.org/10.3390/ijerph17041189S114174Pérez-Ruzafa, A., Pérez-Ruzafa, I. M., Newton, A., & Marcos, C. (2019). 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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. 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