Survival of the synthesis—GPU accelerating evolutionary sound matching

Abstract

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× over the naive serial implementation and 8.88× 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.