perf: optimize audio processing hot paths (mixer, transformers, buffer pool, worker IPC)#210
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quiverdev wants to merge 2 commits into
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perf: optimize audio processing hot paths (mixer, transformers, buffer pool, worker IPC)#210quiverdev wants to merge 2 commits into
quiverdev wants to merge 2 commits into
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- Optimized the core mixing loop in `AudioMixer` to reduce property lookup overhead and avoid per-layer object allocations. - Enhanced `VolumeTransformer` and `FadeTransformer` with specialized paths for constant gain and fast math approximations for the limiter. - Improved `LoudnessNormalizer` efficiency by using pre-calculated reciprocals and optimizing local variable access. - Tuned `BufferPool` constants for better allocation reuse and added a `readTo` method to `RingBuffer` for zero-allocation reads. - Optimized Worker IPC by rounding telemetry statistics to reduce serialization overhead. - Implemented these changes without breaking existing functionality or removing features.
…468960965277556 Performance Optimizations for RAM and CPU Efficiency
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Changes
This PR implements a series of high-impact performance optimizations targeting the hot paths of NodeLink's audio processing engine:
AudioMixer.mixBuffersto pre-filter active layers and use local array references, significantly reducing CPU cycles spent on property lookups and conditional logic.VolumeTransformerandFadeTransformerto skip expensive calculations when gain remains constant between frames.LoudnessNormalizerand implemented a Taylor series approximation for the soft-knee limiter inVolumeTransformer.BufferPoolcapacity to reduce allocation frequency under load. AddedRingBuffer.readToto allow direct copying into existing buffers, avoiding unnecessaryBufferobject allocations.Why
These optimisations collectively improve both CPU throughput and RAM stability by reducing garbage collection pressure and instruction counts in real-time audio streams. The changes make NodeLink more efficient under high concurrency and heavy audio load, which is critical for production deployments.
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