Parallel implementation of the greedy heuristic clustering algorithms

International Workshop "Advanced Technologies in Material Science, Mechanical and Automation Engineering – MIP: Engineering – 2019"

材料科学;机械制造;原子能学

Kazakovtsev, L.A.^1^2 ; Rozhnov, I.P.^1 ; Popov, E.A.^1 ; Karaseva, M.V.^1^2 ; Stupina, A.A.^1^2

Reshetnev Siberian State University of Science and Technology, 31, Krasnoyarskiy Rabochiy avenue, Krasnoyarsk

660031, Russia^1

Siberian Federal University, 79, Svobodny avenue, Krasnoyarsk

660041, Russia^2

关键词: Central processor units;    Computational experiment;    Graphical processing units;    Greedy heuristics;    Large-scale problem;    Parallel implementations;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/537/2/022052/pdf
DOI  :  10.1088/1757-899X/537/2/022052

学科分类：材料科学（综合）

来源: IOP

【 摘 要 】

Authors propose parallel greedy heuristic k-means clustering algorithms for implementation on the graphical processing units (GPU) for solving large-scale problems. The computational experiments illustrate high performance of the GPUs in comparison with running the greedy heuristic algorithms on a central processor unit which is especially significant in the case of big datasets and bug numbers of clusters. The efficiency of the greedy heuristic algorithms in comparison with the standard k-means algorithm remains.