Captain's Log: Stardate 78592.1

The buffer-clearing saga

Adding the new AnukariEffect plugin has ended up precipitating a lot of improvements to Anukari, because it pushed me into testing what happens when multiple instances of the plugin are running at the same time. Most of my testing is done in the standalone Anukari application. It loads extremely quickly, so it's nice for quickly iterating on a new UX change, etc. But in reality, it's likely that users will mostly use Anukari as a plugin, so obviously I need to give that configuration ample attention.

The last big issue I ran into with the plugin was that in GarageBand, loading a song that had something like 6 instances of Anukari and AnukariEffect, sometimes one of the instances would mysteriously fail. The GPU code would initialize just fine, but GPU call to process the first audio block would fail with the very helpful Metal API error, Internal Error (0000000e:Internal Error), unknown reason.

After some research, it turned out that to get a more detailed error from the Metal API, you have to explicitly enable it with MTLCommandBufferDescriptor::errorOptions, and then dig it out of the NSError.userInfo map in an obscure and esoteric manner. So I had my intern (ChatGPT) figure out how to do that and finally I got a "more detailed" error message from the Metal API: IOGPUCommandQueueErrorDomain error 14.

If you've followed my devlog for a while, it should come as no surprise that I am a bit cynical about Apple's developer documentation. So I was completely unsurprised to find that this error is not documented anywhere in Apple's official documents. Apple just doesn't do that sort of thing.

Anyway, I found various mentions of similar errors, with speculation that they were caused by invalid memory accesses, or by kernels that ran too long. I used the Metal API validation tools to check for any weird memory access and they didn't find anything weird. I figured they wouldn't, since I have some pretty abusive fuzz tests that I've run with Metal API validation enabled, and invalid memory access almost certainly would have shown up before.

So I went with the working hypothesis that the kernel was running too long and hitting some kind of GPU watchdog timer. But this was a bit confusing, since the Anukari physics simulation kernel is, for obvious reasons, designed to be extremely fast. With some careful observation and manual bisection of various code features, I realized that it was definitely not the physics kernel, but rather it was the kernel that is used to clear the GPU-internal audio sample buffer.

Some background: Anukari supports audio delay lines, and so it needs to be able to store 1 second of audio history for each Microphone that might be tapped by a delay line. To avoid allocations during real-time audio synthesis, memory is allocated up-front for the maximum number of Microphones, which is 50. But also note that there can be 50 microphones per voice instance, and there can be 16 voice instances. Long story short, the per-microphone, per-instance, per-channel buffer for 1 second of audio is about 300 MB, which is kind of huge.

It's obvious that clearing such a buffer needs to be done locally on the GPU, since transferring a bunch of zeros from the CPU to the GPU would be stupid and slow. So Anukari had a kernel that would clear the buffer at startup, or at other times when it was considered "dirty" due to various possible events (or if the user requested a physics reset).

Now imagine 6 instances of Anukari all being initialized in parallel, and each instance is trying to clear 300 MB of RAM -- that's multiple gigabytes of memory write bandwidth. And sometimes one of those kernels would get delayed or slowed enough to time out. The problem only gets worse with more instances.

Initially I considered a bunch of ideas for how to clear this memory in a more targeted way. We might clear only the memory for microphones that are actually in use. But then we have to track which microphones are live. And also, the way the memory is strided, it's not all that clear that this would help, because we'd still be touching a huge swath of memory.

I came up with a number of other schemes of increasing complexity, which was unsatisfying because complexity is basically my #1 enemy at the moment. Almost all the bugs I'm wrangling at this point have to do with things being so complex that there were corner-cases that I didn't handle.

At this point you might be asking yourself: why does all this memory need to be cleared, anyway? That's a good question, which I should have asked earlier. The simple answer is that if a new delay line is created, we want to make sure that the audio samples it reads are silent in the case that they haven't been written yet by their associated microphone. For example, at startup.

But then that raises the question: couldn't we just avoid reading those audio samples somehow? For example, by storing information about the oldest sample number for which the data in a given sample stream is valid, and consulting that low-watermark before reading the samples.

The answer is yes, we could do that instead. And in a massive face-palm moment, I realized that I had already implemented this timestamp for microphones. So in other words, the memory clearing was completely unnecessary, because the GPU code was already keeping track of the oldest valid audio sample for each stream. I think what happened is that I wrote the buffer-clearing code before the low-watermark code, and forgot to remove the buffer-clearing code. And then forgot that I wrote the low-watermark code.

Well, that's not quite the whole story. In addition to the 50 microphone streams, there are 2 streams to represent the stereo external audio input, which can also be tapped by delay lines (to inject audio into the system as an effect processor). This data did not have a low-watermark, and thus the clearing was important.

However for external audio, a low-watermark is much simpler: it's just sample number 0. This is because external audio is copied into the GPU buffer on every block, and so it never has gaps. The Microphone streams can have gaps, because a Microphone can be deleted and re-added, etc. But for external audio, the GPU code just needs to check that it's not reading anything prior to sample 0, and after that it can always assume the data is valid.

Thus ultimately the fix here was to just add 2 lines of GPU code to check the buffer access for external audio streams, and then to delete a couple hundred lines of CPU/GPU code responsible for clearing the internal buffer, marking it as dirty, etc. This resulted in a noticeable speedup for loading Anukari and completely solved the issue of unreliable initialization in the presence of multiple instances.

Pre-alpha release 0.0.13

With the last reliability bug (that I know of) solved, I was finally able to cut a new pre-alpha release this Friday. I'm super stoked about this release. It has a huge number of crash fixes, bug fixes, and usability enhancements. It also turned out to be the right time to add a few physics features that I felt were necessary before the full release. The details of what's in 0.0.13 are in the release notes and in older devlog entries, so I won't go into them here, but this release is looking pretty dang good.

The next two big things on my radar are AAX support and more factory presets. On the side I've been working to get the AAX certificates, etc., needed to release an AAX plugin, and I think that it should be pretty straightforward to get this working (famous last words). And for factory presets, I have about 50 right now but would like to release with a couple hundred. This is especially important now that I've added AnukariEffect, since only a couple of the current presets are audio effects -- most of them are instruments. So I'm kind of starting from scratch there. I think it's pretty vital to have a really great library of factory presets for both instruments and effects, and also, working on them is a great way to find issues with the plugin.