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Open Access Publications from the University of California

Glift: Generic, Efficient, Random-Access GPU Data Structures

  • Author(s): Lefohn, Aaron;
  • Kniss, Joe M.;
  • Strzodka, Robert;
  • Sengupta, Shubhabrata;
  • Owens, John D.
  • et al.

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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