Speaker
Description
High-density EEG processing is a time-consuming task due to the large number of electrodes, high sampling rates and the computational cost of the pre-processing algorithms. Typical pre-processing steps include high-pass and low-pass filtering, line noise removal, detection and interpolation of bad channels, power spectral density calculation and time-frequency analysis. This talk will present the design and implementation of the multi-GPU convolution operation and the continous wavelet transform for multi-channel EEG analysis. We will highlight the parallel design strategies and stages of the algorithm using the CUDA programming model first to create a single-GPU implementation and then extending it to a multi-node version using data communication schemes based on the Message Passing Interface. In conclusion, multi-GPU runtime measurement results will be presented alongside the performance and scalability of the implementation.
