plb2 VS tarantool

https://github.com/attractivechaos/plb2 - limited but broad comparison across a large number of languages. Swift and Nim both compare favourably to C.

https://github.com/attractivechaos/plb2/blob/master/README.m...

Synthetic benchmarks aside, I think as far as average (spring boots of the world) code goes, Go beats Java almost every time, often in less lines than the usual pom.xml

I wouldn't be so enthusiastic. Look at other languages that have JIT now: Ruby and PHP. After years of efforts, they are still an order of magnitude slower than V8 and even PyPy [1]. It seems to me that you need to design a JIT implementation from ground up to get good performance – V8, Dart, LuaJIT and PyPy are like this; if you start with a pure interpreter, it may be difficult to speed it up later.

[1] https://github.com/attractivechaos/plb2

A curious thing about Swift: after https://github.com/attractivechaos/plb2/pull/23, the matrix multiplication example is comparable to C and Rust. However, I don’t see a way to idiomatically optimise the sudoku example, whose main overhead is allocating several arrays each time solve() is called. Apparently, in Swift there is no such thing as static array allocation. That’s very unfortunate.

The article describes that the new JIT is a "copy-and-patch JIT" (I've previously heard this called a "splat JIT"). This is a relatively simple JIT architecture where you have essentially pre-compiled blobs of machine code for each interpreter instruction that you patch immediate arguments into by copying over them.

I once wrote an article about very simple JITs, and the first example in my article uses this style: https://blog.reverberate.org/2012/12/hello-jit-world-joy-of-...

I take some issue with this statement, made later in the article, about the pros/cons vs a "full" JIT:

> The big downside with a “full” JIT is that the process of compiling once into IL and then again into machine code is slow. Not only is it slow, but it is memory intensive.

I used to think this was true also, because my main exposure to JITs was the JVM, which is indeed memory-intensive and slow.

But then in 2013, a miraculous thing happened. LuaJIT 2.0 was released, and it was incredibly fast to JIT compile.

LuaJIT is undoubtedly a "full" JIT compiler. It uses SSA form and performs many optimizations (https://github.com/tarantool/tarantool/wiki/LuaJIT-Optimizat...). And yet feels no more heavyweight than an interpreter when you run it. It does not have any noticeable warm up time, unlike the JVM.

Ever since then, I've rejected the idea that JIT compilers have to be slow and heavyweight.

Then you should like Tarantool which has a built-in Lua (LuaJIT) application server.

LuaJIT provides Lua language support, including both the language execution environment and the JIT tracer compiler. Our LuaJIT has long differed from the vanilla version in a set of patches adding features, such as the profiler, and new tests. That is why we test our fork thoroughly to prevent regression. LuaJIT source code is open and distributed under a free license, but it does not include regression tests. Therefore, we have assembled our regression test suite from PUC-Rio Lua tests, test suite by François Perrad, tests for other LuaJIT forks, and of course, our own tests.

Starting from 2.10.0-beta Tarantool can natively run on M1 chips. So far this is preliminary support: something may crash or run unstable. We have resolved almost all bugs we knew about, with a few minor ones left. For example, there are some issues with the JIT compiler. But this didn't prevent the team product manager from installing Tarantool on his new MacBook Air and running it every day.

A similar project built with intention around this idea is Tarantool[1]. I never hear much about it, but if you're interested in compute close to your data, this is definitely something that would warrant consideration.

[1]: https://github.com/tarantool/tarantool