pyspeeder

A repository for testing different approach to runtime optimization of Python programs

Testing different approaches to speeding up Python programs, e.g. Numba, c-optimization and so on

Numba

Things to keep in mind when working with numba

• When working with numba and functions that work with lists, it is best to work with numpy arrays instead of Python lists
• When working with numba instead of working with initially empty lists to which elements are appended, it is better to create an initial numpy array of defined type and length; this will help numba
• alternatively, typed list type provided by numba can be applied
• Tosee the impact of this compare example 2 with example 3. Example 3 uses numpy arrays of predefined lengths, while example 2 facilitates the same overall logic with python lists that are initially empty and then are appended to

ThreadPoolExecuter

Things to keep in mind when applying ThreadPoolExecuter from concurrent.futures

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