#76 High-Performance Real-Time FIR-Filtering Using Fast Convolution on Graphics Hardware

In this paper we examine how graphic hardware can be used for real-time FIR filtering. We implement uniformly-partitioned fast convolution in the frequency-domain and evaluate its performance on a NVIDIA GTX 285 graphics card. Motivated by audio rendering for virtual reality, our focus lies on large-scale realtime filtering with a multitude of channels, long impulse responses and low latencies. Graphics hardware has already been used for audio signal processing — including FIR and IIR filtering with respect to offline and real-time processing. However, the combination of GPU computing and real-time conditions leads to a number of challenges that have not been reviewed in detail. The new contribution of this paper is an implementation and detailled analysis of a frequency-domain fast convolution method on GPUs. We discuss specific problems that emerge under real-time conditions. Our method allows to achieve an outstanding real-time filtering performance. In this work, we do not only regard a timeinvariant filtering, but also time-varying filtering, where filters are exchanged during runtime. Furthermore, we examine the opportunities of distributed computation—using CPU and GPU—in order to maximize the performance. Finally, we identify bottlenecks and explain their impact on filter exchange latencies and update rates.