Evaluating the graphics processing unit for digital audio synthesis and the development of HyperModels

The extraordinary growth in computation in single processors for almost half a century is becoming increasingly difficult to maintain. Future computational growth is expected from parallel processors, as seen in the increasing number of tightly coupled processors inside the conventional modern heterogeneous system. The graphics processing unit (GPU) is a massively parallel processing unit that can be used to accelerate particular digital audio processes; However, digital audio developers are cautious of adopting the GPU into their designs to avoid any complications the GPU architecture may have. For example, linear systems simulated using finite-difference-based physical model synthesis is highly suited for the GPU, but developers will be reluctant to use it without a complete evaluation of the GPU for digital audio. Previously limited by computation, the audio landscape could see future advancement by providing a comprehensive evaluation of the GPU in digital audio and developing a framework for accelerating particular audio processes. This thesis is separated into two parts; Part one evaluates the utility of the GPU as a hardware accelerator for digital audio processing using bespoke performance benchmarking suites. The results suggest that the GPU is appropriate under particular conditions; For example, the sample buffer size dispatched to the GPU must be within 32 to 512 to meet real-time digital audio requirements. However, despite some constraints, the GPU could support linear finite-difference-based physical models with 4X higher resolution than the equivalent CPU version. These results suggest that the GPU is superior to the CPU for high-resolution physical models. Therefore, the second part of this thesis presents the design of the novel HyperModels framework to facilitate the development of real-time linear physical models for interaction and performance. HyperModels uses vector graphics to describe a model's geometry and a domain-specific language (DSL) to define the physics equations that operate in the physical model. An implementation of the HyperModels framework is then objectively evaluated by comparing the performance with manually written CPU and GPU equivalent versions. The automatically generated GPU programs from HyperModels were shown to outperform the CPU versions for resolutions 64x64 and above whilst maintaining similar performance to the manually written GPU versions. To conclude part 2, the expressibility and usability of HyperModels is demonstrated by presenting two instruments built using the framewor

HyperModels – A Framework for GPU Accelerated Physical Modelling Sound Synthesis

Physical modelling sound synthesis methods generate vast and intricate sound spaces that are navigated using meaningful parameters. Numerical based physical modelling nsynthesis methods provide authentic representations of the physics they model. Unfortunately, the application of these physical models are often limited because of their considerable computational requirements. In previous studies, the CPU has been shown to reliably support two-dimensional linear finite-difference models in real-time with resolutions up to 64x64. However, the near-ubiquitous parallel processing units known as GPUs have previously been used to process considerably larger resolutions, as high as 512×512 in real-time. GPU programming requires a low-level understanding of the architecture, which often imposes a barrier for entry for inexperienced practitioners. Therefore, this paper proposes HyperModels, a framework for automating the mapping of linear finite-difference based physical modelling synthesis into an optimised parallel form suitable for the GPU. An implementation of the design is then used to evaluate the objective performance of the framework by comparing the automated solution to manually developed equivalents. For the majority of the extensive performance profiling tests, the auto-generated programs were observed to perform only 6\% slower but in the worst-case scenario it was 50\% slower. The initial results suggests that, in most circumstances, the automation provided by the framework avoids the low-level expertise required to manually optimise the GPU, with only a small reduction in performance. However, there is still scope to improve the auto-generated optimisations. When comparing the performance of CPU to GPU equivalents, the parallel CPU version supports resolutions of up to 128x128 whilst the GPU continues to support higher resolutions up to 512x512. To conclude the paper, two instruments are developed using HyperModels based on established physical model designs.</p

Survival of the synthesis—GPU accelerating evolutionary sound matching

Manually configuring synthesizer parameters to reproduce a particular sound is a complex and challenging task. Researchers have previously used different optimization algorithms, including evolutionary algorithms to find optimal sound matching solutions. However, a major drawback to these algorithms is that they typically require large amounts of computational resources, making them slow to execute. This article proposes an optimized design for matching sounds generated by frequency modulation (FM) audio synthesis using the graphics processing unit (GPU). A benchmarking suite is presented for profiling the performance of three implementations: serial CPU, data-parallel CPU, and data-parallel GPU. Results have been collected and discussed from a high-end NVIDIA desktop and a mid-range AMD laptop. Using the default configuration for simple FM, the GPU accelerated design had a speedup of 128 (Formula presented.) over the naive serial implementation and 8.88 (Formula presented.) over the parallel CPU version on a desktop with an Intel i7 9800X CPU and NVIDIA RTX GeForce 2080Ti GPU. Furthermore, the relative speedup over the naive serial implementation continues to increase beyond simple FM to more advanced structures. Further observations include comparisons between integrated and discrete GPUs, toggling optimizations, and scaling evolutionary strategy population size