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

[Nathan Nelson]

I’ve been benchmarking convolution reverb latency on my multi-core rig and hitting some weird scheduling artifacts that I can’t pin down. When I run a floating-point FFT-based convolution at 512-sample blocks for head-tracked spatial audio, CPU spikes jump from ~12% to over 40% on a single core while the rest sit idle. Is this typical thread affinity behavior, or am I looking at a real bottleneck? Specifically, how do you handle block-size mismatches between the main audio thread and the DSP worker when real-time priority gets preempted during background processes?

Second question: for anyone running dedicated DSPs versus CPU-bound convolution, what’s your experience with failure modes under load? I keep seeing tail-clipping on late reflections when I push reverb decay past 3 seconds, and the artifacts correlate with OS thread-switching jitter around 2-4ms. Do you pre-compute impulse responses off-thread, or run real-time convolution? And how do you budget CPU headroom for convolution reverb alongside other real-time tasks without blowing the audio stream deadline? Would love concrete numbers and your mitigation strategies.

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[Gabriel Ward]

Great topic to dig into. The single biggest hidden killer I’ve hit with spatial/audio virtual surround stacks is thread migration, not raw compute. HRTF convolution for 7.1 virtualization can easily chew 8-15% of a core per channel, but what actually makes timing jump is when the audio thread gets yanked between physical cores mid-stream—each migration invalidates the L1/L2 cache and the SMT sibling, spiking latency by 2-4ms per event. In a competitive setup that’s the difference between registering a directional footstep in time or not. My fix was always pinning the audio/render threads with taskset/SetThreadAffinityMask, disabling core parking, and giving the audio thread real-time priority (or

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