Detailed Analysis and Optimization of CUDA K-means Algorithm

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F20%3A10414260" target="_blank" >RIV/00216208:11320/20:10414260 - isvavai.cz</a>
Result on the web
<a href="https://dl.acm.org/doi/abs/10.1145/3404397.3404426" target="_blank" >https://dl.acm.org/doi/abs/10.1145/3404397.3404426</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1145/3404397.3404426" target="_blank" >10.1145/3404397.3404426</a>

Result language
angličtina
Original language name
Detailed Analysis and Optimization of CUDA K-means Algorithm
Original language description
K-means is one of the most frequently used algorithms for unsupervised clustering data analysis. Individual steps of the k-means algorithm include nearest neighbor finding, efficient distance computation, and cluster-wise reduction, which may be generalized to many other purposes in data analysis, visualization, and machine learning. Efficiency of the available implementations of k-means computation steps therefore directly affect many other applications. In this work, we examine the performance limits in the context of modern massively parallel GPU accelerators. Despite the existence of many published papers on this topic, we have found that crucial performance aspects of the GPU implementations remain unaddressed, including the optimizations for memory bandwidth, cache limits, and workload dispatching on problem instances of varying cluster count, dataset size, and dimensionality. We present a detailed analysis of individual computation steps and propose several optimizations that improve the overall performance on contemporary GPU architectures. Our open-source prototype exhibits significant speedup over the current state-of-the-art implementations in virtually all practical scenarios.
Czech name
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Czech description
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Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
ICPP '20: Proceedings of the 49th International Conference on Parallel Processing
ISBN
978-1-4503-8816-0
ISSN
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e-ISSN
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Number of pages
11
Pages from-to
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Publisher name
ACM
Place of publication
New York, NY, USA
Event location
Edmonton AB Canada
Event date
Aug 17, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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