Implementation of Efficient Power Method on CUDA GPU

CUDA 기반 GPU에서 효율적인 Power Method의 구현

  • 김정환 (건국대학교 컴퓨터응용과학부) ;
  • 김진수 (건국대학교 컴퓨터응용과학부)
  • Received : 2010.11.19
  • Accepted : 2010.12.27
  • Published : 2011.02.28


GPU computing is emerging in high performance application area since it can easily exploit massive parallelism in a way of cost-effective computing. The power method which finds the eigen vector of a given matrix is widely used in various applications such as PageRank for calculating importance of web pages. In this research we made the power method efficiently parallelized on GPU and also suggested how it can be improved to enhance its performance. The power method mainly consists of matrix-vector product and it can be easily parallelized. However, it should decide the convergence of the eigen vector and need scaling of the vector subsequently. Such operations incur several calls to GPU kernels and data movement between host and GPU memories. We improved the performance of the power method by means of reduced calls to GPU kernels, optimized thread allocation and enhanced decision operation for the convergence.


Supported by : 건국대학교


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