How to make a fast dynamic language interpreter

Posted by pizlonator 8 hours ago

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Comment by pansa2 3 hours ago

In a similar vein, see this page about the performance of the interpreter for the dynamic language Wren: https://wren.io/performance.html

Unlike the Zef article, which describes implementation techniques, the Wren page also shows ways in which language design can contribute to performance.

In particular, Wren gives up dynamic object shapes, which enables copy-down inheritance and substantially simplifies (and hence accelerates) method lookup. Personally I think that’s a good trade-off - how often have you really needed to add a method to a class after construction?

Comment by versteegen 33 minutes ago

Yes, language design is a hugely important determinant of interpreter or JIT speed. There are many highly optimised VMs for dynamic languages but LuaJIT is king because Lua is such a small and suitable language, and although it does have a couple difficult to optimise features, they are few enough that you can expend the effort. It's nothing like Python. It's not much of an exaggeration to say Python is designed to minimise the possibility of a fast JIT, with compounding layers of dynamism. After years of work, the CPython 3.15 JIT finally managed ~5% faster than the stock interpreter on x86_64.

Comment by psychoslave 2 hours ago

That’s basically what is done all the time in languages where monkey patching is accepted as idiomatic, notably Ruby. Ruby is not known for its speed-first mindset though.

On the other side, having a type holding a closed set of applicable functions is somehow questioning.

There are languages out there that allows to define arbitrary functions and then use them as a methods with dot notation on any variable matching the type of the first argument, including Nim (with macros), Scala (with implicit classes and type classes), Kotlin (with extension functions) and Rust (with traits).

Comment by jiusanzhou 2 hours ago

The jump from change #5 to #6 (inline caches + hidden-class object model) doing the bulk of the work here really tracks with how V8/JSC got fast historically — dynamic dispatch on property access is where naive interpreters die, and everything else is kind of rounding error by comparison. Nice that it's laid out so you can see the contribution of each step in isolation; most perf writeups just show the final number.

Comment by grg0 7 hours ago

Interesting, thanks for sharing. It is a topic I'd like to explore in detail at some point.

I also like how, according to Github, the repo is 99.7% HTML and 0.3% C++. A testament to the interpreter's size, I guess?

Comment by pizlonator 7 hours ago

I committed the statically generated site, which is wastefully large because how I generate the code browsers

But yeah the interpreter is very small

Comment by injidup 3 hours ago

What is this YOLO-c++ compiler that is referenced in the article? Google searches turn up nothing and chatgpt seems not to know it either.

Comment by electroly 3 hours ago

The author of Fil-C, who is also the author of this language, uses "Yolo-C/C++" to mean regular C/C++ without Fil-C.

Comment by tiffanyh 5 hours ago

I see Lua was included, wish LuaJIT was as well.

Comment by pizlonator 5 hours ago

I bet LuaJIT crushes Zef! Or rather, I would hope that it does, given how much more engineering went into it

There are many runtimes that I could have included but didn’t.

Also, it’s quite impressive how much faster PUC Lua is than QuickJS and Python

Comment by raincole 4 hours ago

Because QuickJS is really slow. Don't be fooled by the name. It's almost an order of magnitude slower than node/v8.

(I suppose the quick in QuickJS means "quick for a pure interpreter without JIT compilation or something...)

Comment by pizlonator 4 hours ago

based on this data, it’s probably slower than JSC’s or V8’s interpreter

So like that’s wild

Comment by zephen 5 hours ago

> it’s quite impressive how much faster PUC Lua is than QuickJS and Python

Python's execution time is mostly spent looking up stuff. I don't think lua is quite as dynamic.

Comment by pizlonator 5 hours ago

Lua is way more dynamic

Comment by 2 hours ago

Comment by zephen 4 hours ago

I suppose it depends on where you are looking for dynamicity. In some ways, lua is much more laissez faire of course.

But in Python, everything is an object, which is why, as I said, it spends much of its time looking things up. And things like bindings for closures are late, so that's more lookups as well.

In lua, many things aren't objects, and, for example, you can add two numbers without looking anything up. Another issue, of course, when you do that, is that you could conceivably overflow an integer, but that can't happen in Python either.

The Python interpreter has some fast paths for specific object types, but it is really limited in the optimizations it can do, because there simply aren't any unboxed types.

Comment by psychoslave 1 hour ago

Nop, Python is not full object. Not even Ruby is fully object, try `if.class` for example. Self, Smalltalk, Lisp, and Io are fully object in that sense. But none as far as I know can handle something like `(.class`.

Comment by zephen 42 minutes ago

You obviously realize that different languages have different syntactic requirements, yet you are willing to cut one language a break when its minimal syntactical elements aren't objects, and refuse to cut other languages a break because they have a few more syntactical elements?

Comment by boulos 6 hours ago

How's your experience with Fil-C been? Is it materially useful to you in practice?

Comment by pizlonator 6 hours ago

I’m biased since I’m the Fil.

It was materially useful in this project.

- Caught multiple memory safety issues in a nice deterministic way, so designing the object model was easier than it would have been otherwise.

- C++ with accurate GC is a really great programming model. I feel like it speeds me up by 1.5x relative to normal C++, and maybe like 1.2x relative to other GC’d languages (because C++’s APIs are so rich and the lambdas/templates and class system is so mature).

But I’m biased in multiple ways

- I made Fil-C++

- I’ve been programming in C++ for like 35ish years now

Comment by vlovich123 4 hours ago

I’m curious. Given the overheads of Fil-C++, does it actually make sense to use it for greenfield projects? I like that Fil-C fills a gap in securing old legacy codebases, I’m just not sure I understand it for greenfield projects like this other than you happen to know C++ really well.

Comment by pizlonator 4 hours ago

It made sense because I was able to move very quickly, and once perf became a problem I could move to Yolo-C++ without a full rewrite.

> happen to know C++ really well

That’s my bias yeah. But C++ is good for more than just perf. If you need access to low level APIs, or libraries that happen to be exposed as C/C++ API, or you need good support for dynamic linking and separate compilation - then C++ (or C) are a great choice

Comment by vlovich123 3 hours ago

Hmmm… I did about 20+ years of C++ coding and since I’ve been doing Rust I haven’t seen any of these issues. It has trivial integrations with c/c++ libraries (often with wrappers already written), often better native libraries to substitute those c++ deps wholesale, and separate compilation out of the box. It has dynamic linking if you really need it via the C ABI or even rlib although I’ll grants the latter is not as mature.

The syntax and ownership rules can take some getting used to but after doing it I start to wonder how I ever enjoyed the masochism of the rule of 5 magic incantation that no one else ever followed and writing the class definition twice. + the language gaining complexity constantly without ever paying back tech debt or solving real problems.

Comment by valorzard 2 hours ago

Do you think this exercise has taught you anything that could make fil c itself better?

Comment by Futurmix 7 hours ago

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