jnumpy
truffleruby
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jnumpy | truffleruby | |
---|---|---|
9 | 25 | |
227 | 2,963 | |
0.9% | 0.4% | |
3.9 | 9.9 | |
11 days ago | 3 days ago | |
Julia | Ruby | |
MIT License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
jnumpy
- Making Python 100x faster with less than 100 lines of Rust
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This Week in Python
jnumpy β Writing Python C extensions in Julia within 5 minutes
- GitHub - Suzhou-Tongyuan/jnumpy: Writing Python C extensions in Julia within 5 minutes.
- JNumPy: Writing high-performance C extensions for Python in minutes
truffleruby
- TruffleRuby 24.0.0
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Mir: Strongly typed IR to implement fast and lightweight interpreters and JITs
I think it would be worth mentioning GraalVM and https://github.com/oracle/truffleruby in competitors section.
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GraalVM for JDK 21 is here
GitHub page has some info: https://github.com/oracle/truffleruby#current-status
My question is, how viable is TruffleRuby vs JRuby?
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Making Python 100x faster with less than 100 lines of Rust
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/)
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Ruby 3.2βs YJIT is Production-Ready
Looks like itβs still a WIP
https://github.com/oracle/truffleruby/commits?author=eregon
- Implement Pattern Matching in TruffleRuby (GSoC)
- TruffleRuby β GraalVM Community Edition 22.2.0
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Modern programming languages require generics
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/
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GraalVM 22.1: Developer experience improvements, Apple Silicon builds, and more
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
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Ruby YJIT Ported to Rust
Here's a benchmark [1] done in Jan'22 against many ruby implementations, truffleRuby [2] seems to be way ahead in most, and at least ahead in all. Why truffleRuby isn't talk about much here?
[1] https://eregon.me/blog/2022/01/06/benchmarking-cruby-mjit-yj...
[2] https://github.com/oracle/truffleruby
What are some alternatives?
makepackage - Package for easy packaging of Python code
JRuby - JRuby, an implementation of Ruby on the JVM
ideas
artichoke - π Artichoke is a Ruby made with Rust
poly-match - Source for the "Making Python 100x faster with less than 100 lines of Rust" blog post
graalpython - A Python 3 implementation built on GraalVM
PythonCall.jl - Python and Julia in harmony.
ruby-packer - Packing your Ruby application into a single executable.
log-booster - An VS code extension to quickly add frequently used log statements
graaljs - A ECMAScript 2023 compliant JavaScript implementation built on GraalVM. With polyglot language interoperability support. Running Node.js applications!
Schemathesis - Automate your API Testing: catch crashes, validate specs, and save time
clj-kondo - Static analyzer and linter for Clojure code that sparks joy