[saltcured]

Something like a filter chain for an audio stream is truly the textbook candidate for pipelined concurrency. Conceptually, there are no events or conditional branching. Just a methodical iteration over input samples, in order, producing output samples also in order.

Whatever you can calculate sequentially like:

  while True:
     buf0 = input.recv()
     buf1 = filter1(buf0)
     buf2 = filter2(buf1)
     buf3 = filter3(buf2)
     output.send(buf3)

can instead be written as a set of concurrent worker loops.

Each worker is dedicated to running a specific filter function, so its internal state remains local to that one worker. Only the intermediate sample buffers get relayed between the workers, usually via a low-latency asynchronous queue or similar data structure. If a particular filter function is a little slow, the next stage will simply block on its input receive step until the slow stage can perform the send.

(Edited to try to fix pseudo code block)

[kitchenkarma]

This is how it is typically being done. This is not a problem. Problem is that being concurrent, end to end this process is serial, so you can't process any element of this pipeline in parallel. You can run only so many of those until you run out of time to fill the buffer. I think it could be helpful for you to watch this video: https://www.youtube.com/watch?v=cN_DpYBzKso

[saltcured]

Sorry for the late reply. We have to consider two kinds of latency separately.

A completely sequential process would have a full end-to-end pipeline delay between each audio frame. The first stage cannot start processing a frame until the last stage has finished processing the previous frame. In a real-time system, this turns into a severe throughput limit, as you start to have input/output overflow/underflow. The pipeline throughput is the reciprocal of the end-to-end frame delay.

But, concurrent execution of the pipeline on multiple CPU cores means that you can have many frames in flight at once. The total end-to-end delay is still the sum of the per-stage delays, but the inter-frame delay can be minimized. As soon as a stage has completed one frame, it can start work on the next in the sequence. In such a pipeline, the throughput is the reciprocal of the inter-frame delay for the slowest stage rather than of the total end-to-end delay. The real-time system can scale the number of pipeline stages with the number of CPU cores without encountering input/output overflow/underflow.

Because frame drops were mentioned early on in this discussion, I (and probably others who responded) assumed we were talking about this pipeline throughput issue. But, if your real-time application requires feedback of the results back into a live process, i.e. mixing the audio stream back into the listening environment for performers or audience, then I understand you also have a concern about end-to-end latency and not just buffer throughput.

One approach is to reduce the frame size, so that each frame processes more quickly at each stage. Practically speaking, each frame will be a little less efficient as there is more control-flow overhead to dispatch it. But, you can exploit the concurrent pipeline execution to absorb this added overhead. The smaller frames will get through the pipeline quickly, and the total pipeline throughput will still be high. Of course, there will be some practical limit to how small a frame gets before you no longer see an improvement.

Things like SIMD optimization are also a good way to increase the speed of an individual stage. Many signal-processing algorithms can use vectorized math for a frame of sequential samples, to increase the number of samples processed per cycle and to optimize the memory access patterns too. These modern cores keep increasing their SIMD widths and effective ops/cycle even when their regular clock rate isn't much higher. This is a lot of power left on the table if you do not write SIMD code.

And, as others have mentioned in the discussion, if your filters do not involve cross-channel effects, you can parallelize the pipelines for different channels. This also reduces the size of each frame and hence its processing cost, so the end-to-end delay drops while the throughput remains high with different channels being processed in truly parallel fashion.

Even a GPU-based solution could help. What is needed here is a software architecture where you run the entire pipeline on the GPU to take advantage of the very high speed RAM and cache zones within the GPU. You only transfer input from host to GPU and final results back from GPU to host. You will use only a very small subset of the GPU's processing units, compared to a graphics workload, but you can benefit from very fast buffers for managing filter state as well as the same kind of SIMD primitives to rip through a frame of samples. I realize that this would be difficult for a multi-vendor product with third-party plugins, etc.