This paper focuses on the use of GPGPU (General-Purpose computing on Graphics Processing Units) for audio processing. This is a promising approach to problems where a high parallelization of tasks is desirable. Previous work has examined the application of GPU to the generation of spatial audio. This work aims at extending previous results in view of the most recent technologies updates such as the adoption of PCIe 3.0 and NVIDIA CUDA 5.0 specifications. Within this context we will show a convolution engine having in mind both offline and real-time scenarios, and the support for multiple sound sources. Comparisons between this approach and typical CPU implementations will be presented as well as between frequency (FFT) and time-domain approaches. Results will show that benefits exist in terms of execution time for a number of situations.