Efficient image-aware version control systems using GPU

Summary Version control is considered to be a vital component for supporting professional software development. While it has been widely used for textual artifacts, such as source code or documentation, little attention has been given to binary artifacts. This omission can place huge restrictions on projects in the game and media industries as they contain large amounts of binary data, such as images, videos, three‐dimensional models, and animations, along with their source code. For these kinds of artifacts, existing strategies such as storing the file as a whole for each revision or saving conventional binary deltas consume significant storage space with duplicate data and, even worse, do not provide any understandable information on which modifications were made. As a response to this problem, this paper introduces a change‐set model infrastructure to support version control of image artifacts using a specialized data structure. Additionally, our approach can deal with the maintenance of duplicate nearly identical images through a merge operation. Because of the amount of data that has to be processed, we designed our solution based on a parallel architecture, which permits a massively parallel approach to version control. The paper also compares our approach with some popular open‐source version control systems, showing their repository growth in relation to ours as well as the time required to process image artifacts. Finally, we demonstrate that our architecture requires less storage space and runs much faster than current methods. Copyright © 2015 John Wiley & Sons, Ltd.