For the seasoned Python developer, the notion of performance optimization is a familiar landscape—fraught with challenges yet abundant with opportunities for innovation. While churning out Python code that ‘works’ is easy, crafting code that performs optimally is an art. By Maria Chojnowska.
The article then pays attention to:
- Python code profiling: The key to identifying performance bottlenecks
- cProfile: A standard for performance profiling
- Py-Spy: Sampling profiler for great insights
- Optimizing memory management in Python: Generators and garbage collection
- Generators: Memory efficiency in iterables
- Garbage collection: Manual control
- How to optimize Python loops: Unrolling and vectorization
- Loop unrolling
- Vectorization with NumPy
- Python concurrency: Threading and asynchronous programming for performance
- Threading with “concurrent.futures”
- Asynchronous programming with “asyncio”
- Leveraging GPU acceleration and machine learning for Python performance optimization
- GPU acceleration with CuPy
- Machine learning with TensorFlow
Performance optimization in Python is a rich and rewarding field, laden with opportunities for innovation. Profiling, memory management, loop optimization, parallelism, and leveraging specialized hardware are not just techniques but necessities for anyone serious about Python performance. Good read!
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