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Glift: Generic, efficient, random-access GPU data structures

Aaron E. Lefohn, Joe Kniss, Robert Strzodka, Shubhabrata Sengupta, John D. Owens
University of California, Davis
ACM Transactions on Graphics, Vol. 25 (2006), pp. 60-99

@article{lefohn2006glift,

   title={Glift: Generic, efficient, random-access GPU data structures},

   author={Lefohn, A.E. and Sengupta, S. and Kniss, J. and Strzodka, R. and Owens, J.D.},

   journal={ACM Transactions on Graphics (TOG)},

   volume={25},

   number={1},

   pages={60–99},

   issn={0730-0301},

   year={2006},

   publisher={ACM}

}

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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.
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