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Module 12
Optimize Smart: Performance and Concurrency
Profiling and Timing Python Code
Measure code execution time with `timeit` and profile hotspots using `cProfile` and a visualizer like `snakeviz`.
2
Optimizing Code: Algorithms and Data Structures
Prioritize performance fixes by improving algorithms and choosing appropriate data structures (e.g., `set` for membership tests).
3
Optimizing with Caching and Memoization
Use caching and memoization (`functools.lru_cache`) to avoid re-computing expensive, pure function calls.
Optimizing Memory with Generators and Streaming Pipelines
Refactor memory-intensive code by replacing in-memory lists with generator-based streaming pipelines.
5
Understanding the GIL: Python Concurrency Explained
Explain the role of the Global Interpreter Lock (GIL) and its impact on Python concurrency.
6
Concurrency Models: Threading vs. Multiprocessing
Choose the right concurrency model: `threading` for I/O-bound tasks and `multiprocessing` for CPU-bound tasks.
Optimizing Numeric Computations with NumPy & Cython
Recognize when to offload numeric-heavy computation to optimized libraries like NumPy or Cython.
8
Readable Code, Then Optimize
Avoid premature optimization by focusing on readable code first and only optimizing measured bottlenecks.
End of course,
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