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

Frank Wefers; Jan Berg
DAFx-2010 - Graz
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.