How to make a fast dynamic language interpreter

by pizlonator • 3 months ago

252 60 3 months ago

grg0• 3 months 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?

pizlonator13• 3 months 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

boulos• 3 months ago

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

pizlonator13• 3 months 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

vlovich1233• 3 months 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.

HarHarVeryFunny2• 3 months ago

Are you using malloc + GC in preference to smart pointers, and if so why? I thought Fil-C was just C not C++?

It doesn't seem like that is necessarily a performance win, especially since you could always use a smart pointer's raw pointer (preferably const) in a performance critical path.

thesz• 3 months ago

If I may (and correctly understand what is going inside Fil-C), it would be not so hard to add support for software transactional memory by adding some library calls.

This will greatly reduce coordination bugs in parallel programs and may even speed things up.

tiffanyh• 3 months ago

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

pizlonator13• 3 months 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

zephen5• 3 months 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.

raincole• 3 months 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...)

injidup• 3 months 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.

electroly• 3 months 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.

pansa2• 3 months 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?

psychoslave4• 3 months 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).

pjmlp3• 3 months ago

It is getting better, now that they finally got the Smalltalk lessons from 1984.

"Efficient implementation of the smalltalk-80 system"

https://dl.acm.org/doi/10.1145/800017.800542

IshKebab• 3 months ago

Ruby is not known for its speed-first mindset though.

Or its maintainability, and this is one of the big reasons why. Methods and variables are dynamically generated at runtime which makes it impossible to even grep for them. If you have a large Ruby codebase (say Gitlab or Asciidoctor), it can be almost impossible to trace through code unless you are familiar with the entire codebase.

Their "answer" is that you run the code and use the debugger, but that's clearly ridiculous.

So I would say dynamically defined classes is not only bad for performance; it's just bad in general.

igouy• 3 months ago

Ruby is not known for its speed-first mindset though.

https://benchmarksgame-team.pages.debian.net/benchmarksgame/...

versteegen2• 3 months 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.

dontlaugh• 3 months ago

Python is worse, but not by all that much. After all, PyPy has been several times faster for many years.

pjmlp3• 3 months ago

CPython current state is more a reflection of resources spent, than what is possible.

See experience with Smalltalk and Self, where everything is dynamic dispatch, everything is an object, in a live image that can be monkey patched at any given second.

PyPy and GraalPy, and the oldie IronPython, are much better experiences than where CPython currently stands on.

vlovich1233• 3 months ago

That is an incorrect analysis. CPython is difficult to JIT because of the lack of thought to the native bindings / extensions, not because of the language itself (as others point out PyPy was way faster long ago)

naasking• 3 months ago

In particular, Wren gives up dynamic object shapes, which enables copy-down inheritance and substantially simplifies (and hence accelerates) method lookup.

A general rule of thumb is that if you can assign an expression a static type, then you can compile it fairly efficiently. Complex dynamic languages obviously actively fight this in numerous ways, and so end up being difficult to optimize. Seems obvious in retrospect.

jiusanzhou• 3 months 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.

Someone• 3 months ago

I agree, but there’s a tiny caveat that this is for one specific benchmark that, I think, doesn’t reflect most real-world code.

I’m basing that on the 1.6% improvement they got on speeding up sqrt. That surprised me, because, to get such an improvement, the benchmark must spend over 1.6% of its time in there, to start with.

Looking in the git repo, it seems that did happen in the nbody simulation (https://github.com/pizlonator/zef/blob/master/ScriptBench/nb...).

pizlonator13• 3 months ago

Before that specialization, sqrt calls were hilariously slow - so even calling it sparingly could significantly impact performance.

Basically the flow was:

- check if we’re calling a method of an object

- nope, ok, so cascade through 10+ symbol comparisons

- sqrt was towards the bottom of the cascade

valorzard• 3 months ago

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

pizlonator13• 3 months ago

Yeah I really need to have a better fix for how I handle unions.

And the fact that having outline calls to methods of value objects is so expensive

achierius• 3 months ago

And the fact that having outline calls to methods of value objects is so expensive

Is this tied to unions? Or otherwise, when does this happen? I don't see the connection w/ invisicaps or &c

catlifeonmars• 3 months ago

Do you run an optimization pass on the AST between parsing and evaluation?

pizlonator13• 3 months ago

I run a “resolve” pass.

That’s where for example getter inference happens.

HarHarVeryFunny2• 3 months ago

For an interpreter / AST executor, I think a big win would be efficient parsing in the first place, in particular using a precedence parser for expressions vs recursive descent, which would avoid the need to optimize the AST to remove the 1:1 "unit productions" in the grammar.

tnelsond4• 3 months ago

I use the bounds checker in TCC to check for memory errors in C, should I switch to Fil-C instead to debug my code? Obviously yolo-C is my target.

tikotus• 3 months ago

This is very interesting and well done.

I've gone through something similar, but for a more functional language (a Scheme). It's interesting how here the biggest wins are from optimizing the objects, while the biggest wins in my case were optimizing closures. The optimizations were very similar.

"Three implementation models for scheme" gives all the answers to make a fast enough scheme, though it has something of a compilation step, so it's not interpreting the original AST.

https://www.cs.unm.edu/~williams/cs491/three-imp.pdf

_alphageek• 3 months ago

Good writeup. The Arguments arc (#7→#13) hits close — did basically the same dance for an async step evaluator in Rust a while back. Went all in on Cow<'_, Input> assuming borrow-in-the-common-case would earn its keep. Microbenches looked great. Real workload: the Cow discriminant plus lifetime gunk bled into every combinator past the first await, inlining fell off a cliff, the whole point of Cow evaporated. Ripped it out for NoInput / OneInput<T> / MultiInput(Vec<T>) at the evaluator boundary — same split as your ZeroArguments / OneArgument / TwoArguments, just arrived at the ugly way. One thing I keep wondering: have you stacked arity specialization with type specialization on the native path? Binary<add, int, int> style, drops the isInt probe altogether. Guessing the code size math didn't work out, or ICs are already soaking up whatever's hot on the object side so the native fast paths don't matter much. Which one?

vascocosta2• 3 months ago

Really interesting read, especially after I released the initial version of my own interpreter which is also an AST-walking interpreter. My main goal was to understand at a basic level what it takes to build an interpreted programming language.

I didn't want any optimisation complexities and just focused on being able to understand my own Rust code. I was surprised by the performance I got simply by using my favourite language and as a bonus, since Rust takes care of all the ownership and lifetimes, I don't need a garbage collector. For sure, right now I'm being super conservative and rely on cloning stuff to avoid lifetime hell in stuff like closures, but the speed and memory profile is still very decent.

For anyone interested in a simple to understand tree-walking interpreter in Rust, which is heavily based in expressive enums where code is data, here's my interpreter:

https://gluonscript.org/

lambertsimnel• 3 months ago

GluconScript looks cool, but this sounds too good to be true:

as a bonus, since Rust takes care of all the ownership and lifetimes, I don't need a garbage collector.

I can imagine GluconScript's memory handling comes at a cost, even if the tradeoff of using a borrow checker is well worth it. Was that your experience?

Relatedly, since you commented there has been submission about garbage collectors in Rust ("Garbage Collection Without Unsafe Code"):

https://news.ycombinator.com/item?id=47821853

vascocosta2• 3 months ago

GluonScript really doesn't need a garbage collector, because at the end of the day when a script is executing, it is just safe Rust code evaluating expressions. So it's just a Rust program running really, with its automatic static memory management guarantees.

As far as I researched, only closures could generate dangling references and therefore need memory cleanup, but only if I allowed closures to access their environment (variables and functions) by reference / mutable reference.

To avoid this and simplify both my code as well as the mental model for the users of GluonScript, as of now, closures capture their environment by cloning it immutably. There's an increased memory usage with all the copying of the environment but there are never references to something that isn't being used anymore and therefore no need for a GC. At the end of the day all values captured by closures are owned Rust values that are dropped by Rust when no longer in scope.

So this can lead to high memory usage in hot loops but it can't lead to memory leaks.

How to make a fast dynamic language interpreter

Related Stories