LAPACK Users' guide (third ed.)
LAPACK Users' guide (third ed.)
Independent component analysis: algorithms and applications
Neural Networks
An updated set of basic linear algebra subprograms (BLAS)
ACM Transactions on Mathematical Software (TOMS)
Efficient Independent Component Analysis on a GPU
CIT '10 Proceedings of the 2010 10th IEEE International Conference on Computer and Information Technology
CUDAICA: GPU optimization of infomax-ICA EEG analysis
Computational Intelligence and Neuroscience - Special issue on Advanced Computational Techniques and Tools for Neuroscience
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Several applications in the field of bioinformatics require extracting individual source signals from a large amount of observed data (signal mixtures). Among the available solutions, a possible approach is the independent component analysis (ICA). However, this computationally intensive algorithm does not fit for many real-time or large size data applications. As a result, this shortcoming calls for speeding up the execution of this algorithm. Recently, graphics processing units (GPUs) have emerged as general-purpose parallel processing accelerators. This platform has the potentials to be leveraged in processing a large amount of signals received from medical devices such as EEG and ECG tools. This work provides the implementation of an ICA algorithm, Joint Approximate Diagonalization of Eigen-matrices (JADE), on a low cost programmable graphics cards using CUDA programming toolkits. For this implementation, we achieved an overall speedup of over 7.9x for estimating 64 components, each with 9760 samples.