jnumpy
numexpr
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jnumpy | numexpr | |
---|---|---|
9 | 4 | |
227 | 2,140 | |
0.9% | 0.9% | |
3.9 | 8.2 | |
11 days ago | 27 days ago | |
Julia | Python | |
MIT License | MIT License |
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jnumpy
- Making Python 100x faster with less than 100 lines of Rust
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This Week in Python
jnumpy – 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
numexpr
-
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.
What are some alternatives?
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ideas
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poly-match - Source for the "Making Python 100x faster with less than 100 lines of Rust" blog post
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PythonCall.jl - Python and Julia in harmony.
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