Combining Distributed and Multi-core Programming Techniques to Increase the Performance of K-Means Algorithm

Savvas, Ilias Κ.; Tselios, Dimitrios

doi:10.1109/wetice.2017.21

Cited by 4 publications

(1 citation statement)

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“…Several efforts have been addressed toward parallel implementations of the K -means algorithm in several high-performance computing environments in the last years. Significant algorithms are described, for example, in [ 16 ] for distributed memory architectures, in [ 17 , 18 , 19 ] for multi-core CPUs and in [ 20 ] for GPUs based systems. Almost all these studies emphasize the role of a large amount of data as a critical feature to enable an implementation based on the data parallelism programming model.…”

Section: Introductionmentioning

confidence: 99%

Clustering Algorithms on Low-Power and High-Performance Devices for Edge Computing Environments

Lapegna

Balzano

Meyer

et al. 2021

Sensors

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The synergy between Artificial Intelligence and the Edge Computing paradigm promises to transfer decision-making processes to the periphery of sensor networks without the involvement of central data servers. For this reason, we recently witnessed an impetuous development of devices that integrate sensors and computing resources in a single board to process data directly on the collection place. Due to the particular context where they are used, the main feature of these boards is the reduced energy consumption, even if they do not exhibit absolute computing powers comparable to modern high-end CPUs. Among the most popular Artificial Intelligence techniques, clustering algorithms are practical tools for discovering correlations or affinities within data collected in large datasets, but a parallel implementation is an essential requirement because of their high computational cost. Therefore, in the present work, we investigate how to implement clustering algorithms on parallel and low-energy devices for edge computing environments. In particular, we present the experiments related to two devices with different features: the quad-core UDOO X86 Advanced+ board and the GPU-based NVIDIA Jetson Nano board, evaluating them from the performance and the energy consumption points of view. The experiments show that they realize a more favorable trade-off between these two requirements than other high-end computing devices.

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