This is a collection of promotional and commercial work done by the Symbolics Graphics Division (and customers) showing off the capabilities of the Symbolics LISP Machine.
Lately, I have dedicated a large part of my free time to audio software. I have done this mainly out of interest in the subject due to my history in music. But at the same time, I also thought writing audio software could be a fun passion project or even a small business that I could work on alongside my day job. I don’t see myself replacing my current job with this, but maybe I could dedicate 20% of my work time to it.
The world of audio software is a pretty exciting place. It involves a lot of low-level systems like signals and real-time operations, complex math at times, and something you can feel or at least hear. And what’s great, I don’t have any background in this stuff!
Now I have programmed most of my life and played around with RTOS, but when it comes to writing algorithms for manipulating digital signals, that’s new stuff for me. However, I have experience with the topic from the user point of view since I have been making music for almost as long as I have programmed. This experience involves playing instruments, how effects affect the sound, how mixing and mastering works etc. But what do linear types have to do with any of this?
Signals in the Wild
Like I said earlier, signal processing (not necessarily just audio) is very low-level stuff. So when working with signals in software, you often need to work with C or C++. This is mainly due to the performant and close-to-hardware nature of the languages required to handle and manipulate signals optimally and efficiently.
Digital signal processing is also full of algorithms. The standard workflow for people in this industry seems to be that these applications are prototyped on some high-level language before being produced. Often in languages/tools like MATLAB, Octave, Mathematica, and similar, very heavily math-oriented languages and tools. Julia has appeared to grow in popularity also in this world. These high-level languages are mainly used due to the speed of development.
It is also not uncommon to see FPGA being used in these applications. For several reasons, they are reconfigurable hardware, so you can tailor and deploy computation units and data buses specifically designed for your particular needs. So if you’re working with digital hardware, you can’t go wrong with FPGAs. In this world, VHDL or Verilog comes in handy.
As you can see, overall, the applications tend to involve a lot of different low-level concepts but, at the same time, high-level topics in terms of prototyping. But, as the post’s header might hint, I’m not interested in the prototyping aspects of signal processing since I think those are all well and good. Instead, I’m interested in having a small thought experiment on whether the low-level elements could be improved somehow.
I would consider myself a functional programmer first and foremost, even though I mainly write imperative and/or object-oriented code, at least professionally. Now in my free time and in non-trivial side projects (that are not signal processing related), I like to work with weird languages like Haskell or Common Lisp. Unfortunately, as I mentioned above, almost all the work in this signal-processing world is written in C or C++, emphasizing the latter. However, I completely understand why these languages are used since we talk about real-time programming, so latency needs to be minimized.
“Real-time” can be understood that the program has to produce the correct result but also on a certain amount of time (which varies between systems).
If we use audio processing as an example, typically, you would have some sort of processing function in your code that would work in the audio callback:
process :: BufferRef -> ()
This function would get its callback from a sound card or some input device, e.g. microphone. After it has received its callback, this block of code (whatever might be inside it) would write the corresponding audio data into the given buffer. Which would then be played by the speakers or vice-versa when recording.
This procedure is basically what should happen in real-time lots of times when we are doing audio processing. Audio software is often set up to send these audio callbacks from a high-priority “real-time” thread with a very short latency between the callbacks, ~1-10ms (varies between systems).
To achieve this minimal latency between callbacks, you often can’t rely on garbage collection since you can’t be sure when your program launches it. I dare to say that most of the software benefits from GC significantly, but in the audio, making GC right is very hard. What makes it hard is that if GC launches at the wrong time or the latency between callbacks gets too large, garbage data will leak into the buffer, causing unwanted sounds.
Most other software might only see a slight latency in their computations if they do profiling, so that might not be the end of the world, of course, depending on your context. But in audio, you cannot let that happen since you can literally hear that glitch, which is unforgivable.
When it comes to C or C++, I think everyone knows their foot guns that involve memory management. Thankfully in modern C++, it’s not that bad (as long as you follow core guidelines), but there is still a lot of unnecessary baggage when it comes to safe code in these languages.
Could there be any way we could use garbage-collected language while doing “real-time” operations and how that could be achieved?
introduced support for Linear Haskell, which can be enabled with
-XLinearTypes. One of the significant use cases for linear types is
implementing latency-sensitive real-time services and analytics
jobs. As I mentioned earlier a major issue in this use case is GC
pauses, which can happen at arbitrary points for reasonably long
periods. The problem is exacerbated as the size of the working set
increases. The goal is to partially or entirely eliminate garbage
collection by controlling aliasing and making memory management
explicit but safe.
So what, then, are linear types. Henry Baker described linear types and their benefits in his paper Lively Linear Lisp — ‘Look Ma, No Garbage!’ and also on “Use-once” variables and linear objects: storage management, reflection and multi-threading. As you can see, we are not talking about a new topic. We are talking about types whose instances must be held in a linear variable. A variable is linear if it’s accessed exactly once in its scope. Same for a linear object, their reference count is always 1. When we have this safety guarantee on the type-level, we can avoid synchronization and GC and also, we could update linear objects in place since that would be referentially transparent.
Avoiding Garbage Collection
So why can we avoid synchronization and GC with linear types? If we would consider the following function as an example:
linearFunc :: a %1-> b
On their own, linear types only gives a type to functions that consume their argument exactly once when their result is consumed precisely once. So, alone, they don’t make your programs any faster or safer for resources. Still, they allow you to write many optimizations and resource-safe abstractions that weren’t possible before.
First, since linear values can only be used once, these values cannot be shared. This means that, in principle, they shouldn’t be subject to GC. But this depends on the value consumer since that may very well do some sort of de-allocation on the spot. One way to mitigate this could be to store these values to heap outside of GC’s control.
While utilizing heap for these values alone would diminish the GC, it would introduce some overhead to your program, which could increase the total running time of your application. But if we continue using real-time systems as an example, this isn’t necessarily bad.
In real-time systems, optimizations often happen only to the worst-case scenarios. This is because you don’t really care about your latencies as long as they stay within the particular window. But you watch that those latencies should never go above your maximum limit, and this is primarily where optimisation utilizing linear types could come in handy.
Practical Linear Types
Linear types are a blessing in GC languages if you intend to do anything safely in the low-level world. I would like to continue this post with some practical examples of how Haskell utilizes these types and how they can make low-level optimizations and resources safer in your Haskell code, but that deserves its own post.