Fast image segmentation and smoothing using commodity graphics hardware
Journal of Graphics Tools - Special on hardware-accelerated rendering techniques
Retina simulation using cellular automata and GPU programming
Machine Vision and Applications
Parallelization of cellular neural networks on GPU
Pattern Recognition
Fuzzy ART neural network parallel computing on the GPU
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Programming Massively Parallel Processors: A Hands-on Approach
Programming Massively Parallel Processors: A Hands-on Approach
Pattern Recognition Letters
A survey of medical image registration on graphics hardware
Computer Methods and Programs in Biomedicine
Massively Parallel Neural Signal Processing on a Many-Core Platform
Computing in Science and Engineering
Multi-scale neural texture classification using the GPU as a stream processing engine
Machine Vision and Applications
CUDA optimization strategies for compute- and memory-bound neuroimaging algorithms
Computer Methods and Programs in Biomedicine
Novel Architectures: Solving Computational Problems with GPU Computing
Computing in Science and Engineering
Computer Methods and Programs in Biomedicine
Approximate Entropy-Based Epileptic EEG Detection Using Artificial Neural Networks
IEEE Transactions on Information Technology in Biomedicine
Graphics processing unit (GPU) programming strategies and trends in GPU computing
Journal of Parallel and Distributed Computing
Enhancing GPU parallelism in nature-inspired algorithms
The Journal of Supercomputing
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Cross-Approximate Entropy (Cross-ApEn) is a useful measure to quantify the statistical dissimilarity of two time series. In spite of the advantage of Cross-ApEn over its one-dimensional counterpart (Approximate Entropy), only a few studies have applied it to biomedical signals, mainly due to its high computational cost. In this paper, we propose a fast GPU-based implementation of the Cross-ApEn that makes feasible its use over a large amount of multidimensional data. The scheme followed is fully scalable, thus maximizes the use of the GPU despite of the number of neural signals being processed. The approach consists in processing many trials or epochs simultaneously, with independence of its origin. In the case of MEG data, these trials can proceed from different input channels or subjects. The proposed implementation achieves an average speedup greater than 250x against a CPU parallel version running on a processor containing six cores. A dataset of 30 subjects containing 148 MEG channels (49 epochs of 1024 samples per channel) can be analyzed using our development in about 30min. The same processing takes 5 days on six cores and 15 days when running on a single core. The speedup is much larger if compared to a basic sequential Matlab^(R) implementation, that would need 58 days per subject. To our knowledge, this is the first contribution of Cross-ApEn measure computation using GPUs. This study demonstrates that this hardware is, to the day, the best option for the signal processing of biomedical data with Cross-ApEn.