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Glift: Generic, efficient, random-access GPU data structures
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DOI：10.1145/1122501.1122505.png)
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Abstract
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This article 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 three-dimensional paint. Last, we show that our example Glift data structures perform comparably to handwritten implementations while requiring only a fraction of the programming effort.
Keywords:
algorithms
languages
adaptive
adaptive shadow maps
data structures
graphics hardware
GPU
GPGPU
multiresolution
octree textures
parallel computation
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