Hylton, A., Henselman-Petrusek, G., Sang, J. and Short, R. (2019) Tuning the Performance of a Computational Persistent Homology Package. Software: Practice and Experience, 49, 885-905.
https://doi.org/10.1002/spe.2678

has been cited by the following article:

• TITLE: Developing a Clang Libtooling-Based Refactoring Tool for CUDA GPU Programming

  AUTHORS: Kian Nejadfard, Janche Sang

  KEYWORDS: Refactoring, CUDA, Unified Memory, Clang, Libtooling

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.17 No.2, February 29, 2024

  ABSTRACT: Refactoring tools, whether fully automated or semi-automated, are essential components of the software development life cycle. As software libraries and frameworks evolve over time, it’s crucial for programs utilizing them to also evolve to remain compatible with modern advancements. Take, for example, NVIDIA CUDA’s platform for general-purpose GPU programming. Embracing the more contemporary unified memory architecture offers several benefits, such as simplifying program source code, reducing bugs stemming from manual memory management between host and device memory, and optimizing memory transfer through automated memory handling. This paper describes our development of a refactoring tool based on Clang’s Libtooling to facilitate this transition automatically, thereby relieving developers from the burden and risks associated with manually refactoring large code bases.