Applying GPU parallel programming for image processing and clustering

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This paper presents state-of-the-art image processing and structural analysis software tools that use GPU parallel programming to achieve substantial performance gains. The software suite combines advanced preprocessing techniques, object identification methods, clustering algorithms, and analysis tools to facilitate efficient and precise analysis of complex imaging datasets. The case studies illustrate the software’s versatility and effectiveness across diverse scientific domains, including materials science, biological research, and astronomy. By exploiting GPU parallel programming, the tools deliver performance improvements of 5–20x compared to traditional sequential programming, enabling real-time visualization and expedited data processing. The intuitive user interface empowers researchers to fine-tune parameters, visualize results, and interpret data with ease, streamlining the research workflow. The broader impacts of these tools include accelerating scientific discovery, enhancing data analysis accuracy, and driving innovation across diverse scientific fields. A notable example of their effectiveness is the processing and analysis of electron microscopy images of amorphous alloys. The developed algorithms and software tools demonstrate promising results in this area, facilitating detailed studies of atomic structure and degree of orderliness.

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作者简介

Dagim Dilla

Far Eastern Federal University

编辑信件的主要联系方式.
Email: dilla.d@dvfu.ru
ORCID iD: 0000-0002-9100-1257
SPIN 代码: 7200-1921

postgraduate student, Institute of Mathematics and Computer Technologies; research engineer, Electron Microscopy and Imaging Laboratory

俄罗斯联邦, Vladivostok

Evgeniy Pustovalov

Far Eastern Federal University

Email: pustovalov.ev@dvfu.ru
ORCID iD: 0000-0003-1036-3975
SPIN 代码: 6192-2432

Dr. Sci. (Phys.-Math.); Professor, Department of Information and Computer Systems, Institute of Mathematics and Computer Technologies; Head of the educational program 09.03.02 “Information systems and technologies”, profile “Programming of robotic systems”

俄罗斯联邦, Vladivostok

Irina Artemeva

Far Eastern Federal University

Email: artemeva.il@dvfu.ru
ORCID iD: 0000-0003-2088-5259
SPIN 代码: 8161-1313

Dr. Sci. (Eng.), Professor; deputy Director for Scientific Works, Institute of Mathematics and Computer Technology

俄罗斯联邦, Vladivostok

参考

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  10. Pustovalov E.V., Plotnikov V.S., Grudin B.N. et al. Electron tomography algorithms in scanning transmission electron microscopy. Bull. Russ. Acad. Sci. Phys. 2013. No. 77. Pp. 995–998. doi: 10.3103/S1062873813080340
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  12. Dilla D.S., Pustovalov E.V., Fedorets A.N. Advanced electron microscopy image processing for analyzing amorphous alloys: Electron Microscopy Image Cluster Analyzer (EMICA). Tool and results. Computational Nanotechnology. 2024. Vol. 11. No. 1. Pp. 104–111. doi: 10.33693/2313-223X-2024-11-1-104–111. EDN: DYNPTQ.
  13. Dilla D.S., Pustovalov E.V., Fedorets A.N., Frolov A.M. Exploring amorphous alloys: Advanced electron microscopy and cluster analysis. Computational Nanotechnology. 2024. Vol. 11. No. 1. Pp. 112–120. doi: 10.33693/2313-223X-2024-11-1-112–120. EDN: DYUGCI.
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2. Fig. 1. Modular data analysis architecture: main architectural modules illustrating the data flow from raw data to visualization

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3. Fig. 2. Scatterplot diagram of the NiW sample, showing the identified atomic particle clusters (HRTEM)]

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4. Fig. 3. Linearized Kullback–Leibler (K-L) divergence for the CDF (Div (SP(B/V)) and Lebesgue measure (Div (Mu(B/V)) of the bond-to-vertex ratio, as a function of the ordering level in atomic structure images

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5. Fig. 4. Bond and vertex ratio analysis, showing clusters with a higher degree of ordering as their size increases

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6. Fig. 5. Radial distribution function (RDF) for amorphous alloys, showing particle distance probabilities and revealing local near-order

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7. Fig. 6. Atomic density distribution (a) and radial distribution function (b) for two simulated realizations of the FeB25 alloy

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8. Fig. 7. Evolution of the angle distributions between bonds for different degrees of ordering, from 10 to 90%, in clusters consisting of more than 3 vertices

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9. Fig. 8. Change in the proportion of clusters with certain angles between bonds

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