Parallel computation techniques for virtual acoustics and physical modelling synthesis

The numerical simulation of large-scale virtual acoustics and physical modelling synthesis is a computationally expensive process. Time stepping methods, such as finite difference time domain, can be used to simulate wave behaviour in models of three-dimensional room acoustics and virtual instruments. In the absence of any form of simplifying assumptions, and at high audio sample rates, this can lead to simulations that require many hours of computation on a standard Central Processing Unit (CPU). In recent years the video game industry has driven the development of Graphics Processing Units (GPUs) that are now capable of multi-teraflop performance using highly parallel architectures. Whilst these devices are primarily designed for graphics calculations, they can also be used for general purpose computing. This thesis explores the use of such hardware to accelerate simulations of three-dimensional acoustic wave propagation, and embedded systems that create physical models for the synthesis of sound. Test case simulations of virtual acoustics are used to compare the performance of workstation CPUs to that of Nvidia’s Tesla GPU hardware. Using representative multicore CPU benchmarks, such simulations can be accelerated in the order of 5X for single precision and 3X for double precision floating-point arithmetic. Optimisation strategies are examined for maximising GPU performance when using single devices, as well as for multiple device codes that can compute simulations using billions of grid points. This allows the simulation of room models of several thousand cubic metres at audio rates such as 44.1kHz, all within a useable time scale. The performance of alternative finite difference schemes is explored, as well as strategies for the efficient implementation of boundary conditions. Creating physical models of acoustic instruments requires embedded systems that often rely on sparse linear algebra operations. The performance efficiency of various sparse matrix storage formats is detailed in terms of the fundamental operations that are required to compute complex models, with an optimised storage system achieving substantial performance gains over more generalised formats. An integrated instrument model of the timpani drum is used to demonstrate the performance gains that are possible using the optimisation strategies developed through this thesis.