| title | lang |
|---|---|
Python and High-Performance Computing |
en |
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Python is an interpreted language
- no pre-compiled binaries, all code is translated on-the-fly to machine instructions
- byte-code as a middle step which may be stored (.pyc)
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All objects are dynamic in Python
- nothing is fixed == optimisation nightmare
- lot of overhead from metadata
-
Flexibility is good, but comes with a cost!
- Array based computations with NumPy
- Using extended Cython programming language
- Embed compiled code in a Python program
- C/C++, Fortran
- Utilize parallel processing
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Global Interpreter Lock (GIL)
- CPython's memory management is not thread-safe
- no threads possible, except for I/O etc.
- affects overall performance of threading
-
Process-based "threading" with multiprocessing
- fork independent processes that have a limited way to communicate
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Message-passing is the Way to Go to achieve true parallelism in Python