Abstract:
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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