GPU acceleration for spherical grid tomography

Zhang, Zheyuan

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https://hdl.handle.net/2142/127161 Copy

Description

Title

GPU acceleration for spherical grid tomography

Author(s)

Zhang, Zheyuan

Issue Date

2024-10-23

Director of Research (if dissertation) or Advisor (if thesis)

• Lumetta, Steven Sam
• Butala, Mark

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(s)

• GPU Acceleration
• Solar Tomography

Abstract

Tomography reconstructs cross-sectional images from multi-directional data and plays a crucial role in fields such as medical imaging and solar physics. In solar physics, tomography is crucial for reconstructing 3-D electron density volumes of the solar corona from 2-D coronagraph images. This thesis investigates how factors like grid resolution, the number of viewpoints, and view angles influence reconstruction accuracy and computational cost in solar tomography. Through a series of controlled experiments using synthetic data, we explore the tradeoffs between these factors to provide insights into optimizing future solar tomography tasks. To enable these computationally intensive experiments, we develop a GPU-accelerated forward model framework specifically designed for spherical grids. This framework achieves a significant speedup of 27x to 270x compared to sequential implementations, allowing us to conduct large-scale simulations and reconstructions efficiently. This work not only advances the understanding of the solar tomography reconstruction but also paves the way for future advancements in space tasks.

Graduation Semester

2024-12

Type of Resource

Thesis

Handle URL

https://hdl.handle.net/2142/127161

Copyright and License Information

Copyright 2024 Zheyuan Zhang

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