Understanding Hardware-Accelerated 2D Vector Graphics

ASU Author/Contributor (non-ASU co-authors, if there are any, appear on document)
Spencer C. Imbleau (Creator)
Appalachian State University (ASU )
Web Site: https://library.appstate.edu/
R. Mitchell Parry

Abstract: With the rising support of compute kernels and low-level GPU architecture access over the past few years, friction with general-purpose GPU computing is fading. With new accessibility, new analytics methods for hardware-accelerated vector rasterization are being tried with new leverage. There are compelling reasons to optimize performance given the resolution-independent imaging model and inherent benefits. However, there is a noticeable lack of comparison between algorithms, techniques, and libraries which gauge the modern rendering capability. Analyzing the performance of vector graphics on the GPU is challenging, primarily when various technologies may compete for differing scarce computer resources. This thesis examines the contention found with modern vector graphic rendering and expands on analysis techniques used to de-obfuscate efficacy by providing an analytic benchmarking framework for hardware-accelerated renderers.

Additional Information

Imbleau, S. (2022). Understanding Hardware-Accelerated 2D Vector Graphics. Unpublished Master’s Thesis. Appalachian State University, Boone, NC.
Language: English
Date: 2022
gpu, rendering, 2D graphics, vector graphics, benchmarking

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