Spatial Audio Processing Overhead on Multi-Core CPUs and Dedicated DSPs: Advanced Implementation and Real-World Architecture

[Sam Young]

I’ve been benchmarking spatial audio pipelines on my current rig and I’m hitting a wall on CPU budget allocation. My setup uses a floating-point FFT-based convolution engine running on a high-core-count CPU, and I’m seeing convolution reverb costs spike unpredictably under load. The real question is: how are you handling thread scheduling latency when routing spatialized audio through multiple cores? I’ve tried pinning DSP threads to dedicated cores with affinity masks, but I’m still seeing jitter that messes with low-latency competitive play.

For those running dedicated hardware DSPs versus software-based solutions—what’s your actual split on overhead? I’m measuring ~30% CPU utilization just for real-time HRTF processing, which feels excessive. Are people successfully offloading convolution to dedicated DSP cores, or is software convolution on modern multicore CPUs finally viable for latency-sensitive applications? And specifically, what buffer sizes and thread priorities are you running to keep scheduling stalls under control? Any hard benchmark numbers comparing hardware vs. software routing would be incredibly helpful—I’m looking for sub-5ms total latency targets.

[Daniel Murphy]

On the CPU side, the real cost of spatial audio isn’t the HRTF convolution itself—it’s thread scheduling and memory bandwidth. When you’re running object-based

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"Positioning beats raw aim 9 times out of 10."

[Mason Gray]

Look, the real bottleneck nobody talks about is that spatial audio convolution isn’t parallelizable the way you’d hope. Each binaural render pass runs HRTF FIR filters, and the naive direct convolution is O(N·M) per source per frame. With 32+ active 3D sources in a modern shooter, you’re doing thousands of multiply-accumulate operations every single frame, and the problem is the dependency chain — audio is inherently sequential, sample-by-sample, so you can’t just throw more cores at it. What actually helps is FFT-based overlap-add convolution (usually 1024-2048 point), which turns that into O(N log N), but then you’re trading CPU for latency.

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