David A. Bader
IEEE Fellow
AAAS Fellow
Professor
College of Computing
Georgia Tech
Atlanta, GA 30332


 
 

 

GPUMemSort: A High Performance Graphic Co-processors Sorting Algorithm for Large Scale In-Memory Data

In this paper, we present a GPU-based sorting algorithm, GPUMemSort, which achieves high performance in sorting large-scale in-memory data by exploiting high-parallel GPU processors. It consists of two algorithms: in-core algorithm, which is responsible for sorting data in GPU global memory efficiently, and out-of-core algorithm, which is responsible for dividing large scale data into multiple chunks that fit GPU global memory. GPUMemSort is implemented based on NVIDIA CUDA framework and some critical and detailed optimization methods are also presented. The tests of different algorithms have been run on multiple data sets. The experimental results show that our in-core sorting can outperform other comparison-based algorithms and GPUMemSort is highly effective in sorting large-scale in-memory data.

Publication History

Versions of this paper appeared as:
  1. Yin Ye, Zhihui Du, and David A. Bader. ``GPUMemSort: A High Performance Graphic Co-processors Sorting Algorithm for Large Scale In-Memory Data,'' Annual International Conference on Advances in Distributed and Parallel Computing (ADPC 2010), Singapore, November 1-2, 2010.
  2. Yin Ye, Zhihui Du, David A. Bader, Quan Yang, and Weiwei Huo. ``GPUMemSort: A High Performance Graphic Co-processors Sorting Algorithm for Large Scale In-Memory Data,'' GSTF International Journal on Computing, 1(2):23-28, 2011.

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Last updated: June 1, 2012

 




Computational Biology



Parallel Computing



Combinatorics