Fast Parallel GPU-Sorting Using a Hybrid Algorithm
Paper in proceeding, 2007

This paper presents an algorithm for fast sorting of large lists using modern GPUs. The method achieves high speed by efficiently utilizing the parallelism of the GPU throughout the whole algorithm. Initially, a parallel bucketsort splits the list into enough sublists then to be sorted in parallel using merge-sort. The parallel bucketsort, implemented in NVIDIA’s CUDA, utilizes the synchronization mechanisms, such as atomic increment, that is available on modern GPUs. The mergesort requires scattered writing, which is exposed by CUDA and ATI’s Data Parallel Virtual Machine[1]. For lists with more than 512k elements, the algorithm performs better than the bitonic sort algorithms, which have been considered to be the fastest for GPU sorting, and is more than twice as fast for 8M elements. It is 6-14 times faster than single CPU quicksort for 1-8M elements respectively. In addition, the new GPU-algorithm sorts on n log n time as opposed to the standard n(log n)2 for bitonic sort. Recently, it was shown how to implement GPU-based radix-sort, of complexity n log n, to outperform bitonic sort. That algorithm is, however, still up to ∼ 40% slower for 8M elements than the hybrid algorithm presented in this paper. GPU-sorting is memory bound and a key to the high performance is that the mergesort works on groups of four-float values to lower the number of memory fetches. Finally, we demonstrate the performance on sorting vertex distances for two large 3D-models; a key in for instance achieving correct transparency.


Erik Sintorn

Chalmers, Computer Science and Engineering (Chalmers), Computer Engineering (Chalmers)

Ulf Assarsson

Chalmers, Computer Science and Engineering (Chalmers), Computer Engineering (Chalmers)

GPGPU 2007 - Workshop on General Purpose Processing on Graphics Processing Units

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