Institute for Data Analysis and Visualization
Glift: Generic, Efficient, Random-Access GPU Data Structures
- Author(s): Lefohn, Aaron
- Kniss, Joe M.
- Strzodka, Robert
- Sengupta, Shubhabrata
- Owens, John D.
- et al.
Published Web Locationhttps://doi.org/10.1145/1122501.1122505
This paper presents Glift, an abstraction and generic template library for defining complex, random-access graphics processor (GPU) data structures. Like modern CPU data structure libraries, Glift enables GPU programmers to separate algorithms from data structure definitions; thereby greatly simplifying algorithmic development and enabling reusable and interchangeable data structures. We characterize a large body of previously published GPU data structures in terms of our abstraction and present several new GPU data structures. The structures, a stack, quadtree, and octree, are explained using simple Glift concepts and implemented using reusable Glift components. We also describe two applications of these structures not previously demonstrated on GPUs: adaptive shadow maps and octree 3D paint. Lastly, we show that our example Glift data structures perform comparably to handwritten implementations while requiring only a fraction of the programming effort.