poly-match VS truffleruby

Not my experience at all. At work we rewrote a small bit of hotspot python in Rust with no issues. This was what we primarily followed: https://ohadravid.github.io/posts/2023-03-rusty-python/

Take at look at this example, it still uses Python as an interface to Rust code. Maybe you can do something similar to still achieve performance improvements without changing the entire codebase.

People are probably downvoting because it's needlessly hyperbolic and argumentative. Nobody is saying that python isn't faster to iterate with, but they're arguing that it would take months to get negligable performance gains in a lower level language, meanwhile here is a recent post from a company that increased the execution of they're python code by 100x with less than 100 lines of Rust. They also claim that nobody cares if something runs a few milliseconds faster, when we're talking about game dev, where games are frequently judged on how many milliseconds it takes to run game logic between frames.

Semi Vectorized code:

https://github.com/ohadravid/poly-match/blob/main/poly_match...

Expecting Python engineers unable to read defacto standard numpy code but meanwhile expect everyone can read Rust.....

Not to mention that the semi-vectorized code is still suboptimal. Too many for loops despite the author clearly know they can all be vectorized.

For example instead the author can just write something like:

   np.argmin(

The article links to a full implementation, so you should be able to test this.

I think it would be worth mentioning GraalVM and https://github.com/oracle/truffleruby in competitors section.

GitHub page has some info: https://github.com/oracle/truffleruby#current-status

My question is, how viable is TruffleRuby vs JRuby?

I wonder why GraalVM is not more often used for these speed critical cases: https://www.graalvm.org/python/

Is the problem the Oracle involvement? (Same for ruby https://www.graalvm.org/ruby/)

Looks like it’s still a WIP

https://github.com/oracle/truffleruby/commits?author=eregon

this comes at the cost of boxing ints inside Integer, though.

So, if you ignore for a moment primitives types, whenever you have generics, everything boils down to a single method accepting Objects and returning Objects. What the JVM does is to do runtime profiling of what actually you are passing to the generic method, and generate optimized routines for the "best case". In theory this is the best of the two worlds, because like in general you will have a single implementation of the method (avoiding duplication of the code), but if you use it in an hot spot you get the optimized code.

In a way, it is quite wasteful, because you throw away a lot of information at compile time, just to get it back (and maybe not all of it) at runtime through profiling, but in practice it works quite well.

A side effect of this is this makes the JVM a wonderful VM for running dynamic languages like Ruby and Python, because that information is _not_ there at compile time. In particular GraalVM/TruffleVM and exposes this functionality to dynamic language implementations, allowing very good performance (according to they website [1][2], Ruby and Python on TruffleVM are about 8x faster than the official implementation, and JS in line with V8)

[1] https://www.graalvm.org/ruby/

I opened a ticket some time ago about performance with Jekyll and liquid templates. At least in that case, yjit was way faster. I'm happy to retest though. Anything that would make my jekyll builds faster would help.

https://github.com/oracle/truffleruby/issues/2363