librope VS Graal

Yep. A few years ago I implemented a skip list based rope library in C[1], and after learning rust I eventually ported it over[2].

The rust implementation was much less code than the C version. It generated a bigger assembly but it ran 20% faster or so. (I don't know why it ran faster than the C version - this was before the noalias analysis was turned on in the compiler).

Its now about 3x faster than C, thanks to some use of clever layered data structures. I could implement those optimizations in C, but I find rust easier to work with.

C has advantages, but performance is a bad reason to choose C over rust. In my experience, the runtime bounds checks it adds are remarkably cheap from a performance perspective. And its more than offset by the extra optimizations the rust compiler can do thanks to the extra knowledge the compiler has about your program. If my experience is anything to go by, naively porting C programs to rust would result in faster code a lot of the time.

And I find it easier to optimize rust code compared to C code, thanks to generics and the (excellent) crates ecosystem. If I was optimizing for runtime speed, I'd pick rust over C every time.

[1] https://github.com/josephg/librope

[2] https://github.com/josephg/jumprope-rs

> it’s not clear if this will be a positive for native dev advocacy

I've rewritten a few things in rust. Seems pretty positive to me, because you can mix some of the best optimizations and data structures you'd write in C, with much better developer ergonomics.

A few years ago I wrote a rope library in C. This is a library for making very fast, arbitrary insert & delete operations in a large string. My C code was about as fast as I could make it at the time. But recently, I took a stab at porting it to Rust to see if I could improve things. Long story short, the rust version is another ~3x faster than the C version.

https://crates.io/crates/jumprope

(Vs in C: https://github.com/josephg/librope )

The competition absolutely isn't fair. In rust, I managed to add another optimization that doesn't exist in the C code. I could add it in C, but it would have been really awkward to weave in. Possible, but awkward in an already very complex bit of C. In rust it was much easier because of the language's ergonomics. In C I'm using lots of complex memory management and I don't want to add complexity in case I add memory corruption bugs. In rust, well, the optimization was entirely safe code.

And as for other languages - I challenge anyone to even approach this level of performance in a non-native language. I'm processing ~30M edit operations per second.

But these sort of performance results probably won't scale for a broader group of programmers. I've seen rust code run slower than equivalent javascript code because the programmers, used to having a GC, just Box<>'ed everything. And all the heap allocations killed performance. If you naively port python line-by-line to rust, you can't expect to magically get 100x the performance.

Its like, if you give a top of the line Porsche to an expert driver, they can absolutely drive faster. But I'm not an expert driver, so I'll probably crash the darn thing. I'd take a simple toyota or something any day. I feel like rust is the porsche, and python is the toyota.

> I have no idea whether that matters or even easy to measure...

It is reasonably easy to measure, and the GP is about right. I've measured a crossover point of around a few hundred items too. (Though I'm sure it'll vary depending on use case and whatnot.)

I made a rope data structure a few years ago in C. Its a fancy string data structure which supports inserts and deletes of characters at arbitrary offsets. (Designed for text editors). The implementation uses a skip list (which performs similarly to a b-tree). At every node we store an array of characters. To insert or delete, we traverse the structure to find the node at the requested offset, then (usually) memmove a bunch of characters at that node.

Q: How large should that per-node array be? A small number would put more burden on the skip list structure and the allocator, and incur more cache misses. A large number will be linearly slower because of all the time spent in memmove.

Benchmarking shows the ideal number is in the ballpark of 100-200, depending on CPU and some specifics of the benchmark itself. Cache misses are extremely expensive. Storing only a single character at each node (like the SGI C++ rope structure does) makes it run several times slower. (!!)

Code: https://github.com/josephg/librope

This is the constant to change if you want to experiment yourself:

https://github.com/josephg/librope/blob/81e1938e45561b0856d4...

In my opinion, hash tables, btrees and the like in the standard library should probably swap to flat lists internally when the number of items in the collection is small. I'm surprised more libraries don't do that.

Contrary to what vocal Kotlin advocates might believe, Kotlin only matters on Android, and that is thanks to Google pushing it no matter what.

https://spectrum.ieee.org/the-top-programming-languages-2023

https://snyk.io/reports/jvm-ecosystem-report-2021/

And even so, they had to conceed Android and Kotlin on their own, without the Java ecosystem aren't really much useful, thus ART is now updatable via Play Store, and currently supports OpenJDK 17 LTS on Android 12 and later devices.

As for your question regarding numbers, mostly Java 74.6%, C++ 13.7%, on the OpenJDK, other JVM implementations differ, e.g. GraalVM is mostly Java 91.8%, C 3.6%.

https://github.com/openjdk/jdk

https://github.com/oracle/graal

Two examples from many others, https://en.wikipedia.org/wiki/List_of_Java_virtual_machines

Pkl was built using the GraalVM Truffle framework. So it supports runtime compilation using Futurama Projections. We have been working with Apple on this for a while, and I am quite happy that we can finally read the sources!

https://github.com/oracle/graal/tree/master/truffle

Disclaimer: graalvm dev here.

That's pretty interesting. It's not as aggressive as Bee sounds, but the Espresso JVM is somewhat similar in concept. It's a full blown JVM written in Java with all the mod cons, which can either be compiled ahead of time down to memory-efficient native code giving something similar to a JVM written in C++, or run itself as a Java application on top of another JVM. In the latter mode it obviously doesn't achieve top-tier performance, but the advantage is you can easily hack on it using all the regular Java tools, including hotswapping using the debugger.

When run like this, the bytecode interpreter, runtime system and JIT compiler are all regular Java that can be debugged, edited, explored in the IDE, recompiled quickly and so on. Only the GC is provided by the host system. If you compile it to native code, the GC is also written in Java (with some special conventions to allow for convenient direct memory access).

What's most interesting is that Espresso isn't a direct translation of what a classical C++ VM would look like. It's built on the Truffle framework, so the code is extremely high level compared to traditional VM code. Details like how exactly transitions between the interpreter/compiled code happen, how you communicate pointer maps to the GC and so on are all abstracted away. You don't even have to invoke the JIT compiler manually, that's done for you too. The only code Espresso really needs is that which defines the semantics of the Java bytecode language and associated tools like the JDWP debugger protocol.

https://github.com/oracle/graal/tree/master/espresso

This design makes it easy to experiment with new VM features that would be too difficult or expensive to implement otherwise. For example it implements full hotswap capability that lets you arbitrarily redefine code and data on the fly. Espresso can also fully self-host recursively without limit, meaning you can achieve something like what's described in the paper by running Espresso on top of Espresso.

I'm also using GraalVM if that's of any help.

Quarkus is one of Java frameworks for microservices development and cloud-native deployment. It is developed as container-first stack and working with GraalVM and HotSpot virtual machines (VM).

Apologies, I didn't mean to imply DCEVM went poof, just that I was sad it didn't make it into OpenJDK so one need not do JDK silliness between the production one and the "debugging one" since my experience is that's an absolutely stellar way to produce Heisenbugs

And I'll be straight: Graal scares me 'cause Oracle but I just checked and it looks to the casual observer that it's straight-up GPLv2 now so maybe my fears need revisiting: https://github.com/oracle/graal/blob/vm-23.1.0/LICENSE

> to be compiled to a single executable is a strength that Java does not have

I think this is very outdated claim: https://www.graalvm.org/

https://github.com/oracle/graal/issues/7182