Spatial Audio Processing Overhead on Multi-Core CPUs and Dedicated DSPs: Edge Cases, Failure Modes, and Root-Cause Debugging

[Mason Peterson]

I’ve been profiling my spatial audio pipeline and the floating-point convolution reverb is eating my CPU budget in ways I can’t fully explain. On my 12-core setup, I’m seeing diminishing returns past 6 threads when running multiple simultaneous impulse-response convolutions, and the tail latency spikes are inconsistent enough that I suspect thread scheduling jitter rather than raw compute saturation. For anyone who’s actually benchmarked convolution reverb stacks on multi-core CPUs—how are you chunking the FFT blocks, and at what IR length do you start seeing the crossover where single-threaded becomes competitive? Also, how much real-world latency are you measuring from thread handoff between the audio thread and the convolution worker pool?

On the DSP side, I’m torn between offloading to a dedicated audio DSP versus keeping it all on-CPU for lower latency. Has anyone hit actual failure modes where the DSP path introduces more problems than it solves—buffer underruns, fixed-point quantization artifacts, or scheduling deadlocks under load? And what mitigation strategies actually held up when you pushed the system into edge cases like rapid IR swaps or sudden thread count changes mid-session?

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