numexpr
truffleruby
numexpr | truffleruby | |
---|---|---|
4 | 26 | |
2,143 | 2,963 | |
0.7% | 0.1% | |
8.2 | 9.9 | |
about 1 month ago | 7 days ago | |
Python | 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.
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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.
numexpr
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Making Python 100x faster with less than 100 lines of Rust
You can just slap numexpr on top of it to compile this line on the fly.
https://github.com/pydata/numexpr
- Extending Python with Rust
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[D] How to avoid CPU bottlenecking in PyTorch - training slowed by augmentations and data loading?
Are you doing any costly chained NumPy operations in your preprocessing? E.g. max(abs(large_ary)), this produces multiple copies of your data, https://github.com/pydata/numexpr can greatly reduce time spent with such operations
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Selection in pandas using query
What is not entirely obvious here is that under the hood you can install a nice library called numexpr (docs, src) that exists to make calculations with large NumPy (and pandas) objects potentially much faster. When you use query or eval, this expression is passed into numexpr and optimized using its bag of tricks. Expected performance improvement can be between .95x and up to 20x, with average performance around 3-4x for typical use cases. You can read details in the docs, but essentially numexpr takes vectorized operations and makes them work in chunks that optimize for cache and CPU branch prediction. If your arrays are really large, your cache will not be hit as often. If you break your large arrays into very small pieces, your CPU won’t be as efficient.
truffleruby
- Rails Core Classes Method Lookup Changes: A Deep Dive into Include vs Prepend
- 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 3.2’s YJIT is Production-Ready
Looks like it’s still a WIP
https://github.com/oracle/truffleruby/commits?author=eregon
- Implement Pattern Matching in TruffleRuby (GSoC)
- TruffleRuby – 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
What are some alternatives?
pytorch-lightning - Build high-performance AI models with PyTorch Lightning (organized PyTorch). Deploy models with Lightning Apps (organized Python to build end-to-end ML systems). [Moved to: https://github.com/Lightning-AI/lightning]
JRuby - JRuby, an implementation of Ruby on the JVM
pygfx - A python render engine running on wgpu.
artichoke - 💎 Artichoke is a Ruby made with Rust
greptimedb - An open-source, cloud-native, distributed time-series database with PromQL/SQL/Python supported. Available on GreptimeCloud.
graalpython - A Python 3 implementation built on GraalVM
jnumpy - Writing Python C extensions in Julia within 5 minutes.
ruby-packer - Packing your Ruby application into a single executable.
jsmpeg - MPEG1 Video Decoder in JavaScript
graaljs - A ECMAScript 2023 compliant JavaScript implementation built on GraalVM. With polyglot language interoperability support. Running Node.js applications!
poly-match - Source for the "Making Python 100x faster with less than 100 lines of Rust" blog post
clj-kondo - Static analyzer and linter for Clojure code that sparks joy