Parallel GPU Implementation of Iterative PCA Algorithms

@article{Andrecut2009ParallelGI,
  title={Parallel GPU Implementation of Iterative PCA Algorithms},
  author={M. Andrecut},
  journal={Journal of computational biology : a journal of computational molecular cell biology},
  year={2009},
  volume={16 11},
  pages={
          1593-9
        }
}
  • M. Andrecut
  • Published 2009
  • Biology, Computer Science, Physics, Medicine
  • Journal of computational biology : a journal of computational molecular cell biology
Principal component analysis (PCA) is a key statistical technique for multivariate data analysis. [...] Key Method Here we present an algorithm based on Gram-Schmidt orthogonalization (called GS-PCA), which eliminates this shortcoming of NIPALS-PCA. Also, we discuss the GPU (Graphics Processing Unit) parallel implementation of both NIPALS-PCA and GS-PCA algorithms. The numerical results show that the GPU parallel optimized versions, based on CUBLAS (NVIDIA), are substantially faster (up to 12 times) than the…Expand
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References

SHOWING 1-4 OF 4 REFERENCES
Efficient Gram–Schmidt orthonormalisation on parallel computers
TLDR
The paper shows how these algorithms can be implemented on a parallel computer, and how their communication overhead can be minimized, and provides some guidelines for selecting the most appropriate algorithm. Expand
A User's Guide to Principal Components
Preface.Introduction.1. Getting Started.2. PCA with More Than Two Variables.3. Scaling of Data.4. Inferential Procedures.5. Putting It All Together-Hearing Loss I.6. Operations with Group Data.7.… Expand
Loss and Recapture of Orthogonality in the Modified Gram-Schmidt Algorithm
TLDR
The special structure of the product of the Householder transformations is derived, and then used to explain and bound the loss of orthogonality in MGS, which is illustrated by deriving a numerically stable algorithm based on MGS for a class of problems which includes solution of nonsingular linear systems. Expand
Principal component analysis
Principal Component Analysis (PCA) is a multivariate exploratory analysis method, useful to separate systematic variation from noise. It allows to define a space of reduced dimensions that preserve… Expand