CudaFilters: A SignalPlant library for GPU-accelerated FFT and FIR filtering

摘要

Signal filtering is one of the essential tasks in signal processing. It may become an extremely time-consuming process, as in the case of intracranial electroencephalogram recordings (eg, 30-min records) with a large number of channels (up to 256) and high sampling frequencies (up to 5kHz in research related to ultra-high-frequency oscillations). The usual way of dealing with time consumption is process parallelization. Moreover, parallelization using graphic processing unit (GPU) allows further shortening of computing times thanks to the large number of GPU cores. This paper describes a library for GPU-accelerated finite impulse response (FIR) and fast Fourier transform (FFT) filteringCudaFilters. This library is designed for SignalPlant softwarea free tool for signal analysis. The resultant acceleration in computing times was 5x to 40x depending on the task, data, and hardware configuration. The results were also compared to computing speeds in Matlab.