Fast ICA on Modern GPU Architectures

Blind Signal Separation is an algorithmic problem class that deals with the restoration of original signal data from a signal mixture. Implementations, such as Fast ICA, are optimized for parallelization on CPU or first-generation GPU hardware. With the advent of modern, compute centered GPU hardware with powerful features such as dynamic parallelism support, these solutions no longer leverage the available hardware performance in the best-possible way. We present an optimized implementation of the FastICA algorithm, which is specifically tailored for next-generation GPU architectures such as Nvidia Kepler. Our proposal achieves a two digit factor of speedup in the prototype implementation, compared to a multithreaded CPU implementation. Our custom matrix multiplication kernels, tailored specifically for the use case, contribute to the speedup by delivering better performance than the state-of-the-art CUBLAS library.