Fast Parallel Sorting Algorithms on GPUs

Bilal Jan, Bartolomeo Montrucchio, Carlo Ragusa, Fiaz Gul Khan, Omar Khan
Dipartimento di Automatica e Informatica, Politecnico di Torino, Torino, I-10129 Italy
International Journal of Distributed and Parallel systems (IJDPS), Volume 3, Number 6, 2012



   author={Jan, B. and Montrucchio, B. and Ragusa, C. and Khan, F.G. and Khan, O.},



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This paper presents a comparative analysis of the three widely used parallel sorting algorithms: OddEven sort, Rank sort and Bitonic sort in terms of sorting rate, sorting time and speed-up on CPU and different GPU architectures. Alongside we have implemented novel parallel algorithm: min-max butterfly network, for finding minimum and maximum in large data sets. All algorithms have been implemented exploiting data parallelism model, for achieving high performance, as available on multi-core GPUs using the OpenCL specification. Our results depicts minimum speed-up19x of bitonic sort against oddeven sorting technique for small queue sizes on CPU and maximum of 2300x speed-up for very large queue sizes on Nvidia Quadro 6000 GPU architecture. Our implementation of full-butterfly network sorting results in relatively better performance than all of the three sorting techniques: bitonic, odd-even and rank sort. For min-max butterfly network, our findings report high speed-up of Nvidia quadro 6000 GPU for high data set size reaching 224 with much lower sorting time.
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