A guide to accelerating NumPy computations using parallel processing with thread pools and Numba for optimized performance.
A technical benchmark comparing Python, NumPy, Numba, Rust, and Mojo for simulating permutation cycles, with corrected results.
Learn how to write tests to verify that compiler optimizations, like loop elimination, are actually applied to your code.
Learn how to use Numba's @jit decorator to achieve massive speedups in Python code with minimal changes, demonstrated with a benchmark example.
A guide to optimizing a non-trivial algorithm (NUFFT) in Python using NumPy and Numba, comparing performance to a Fortran implementation.
A performance comparison revisiting Numba vs. Cython for optimizing a pairwise distance array computation, using updated libraries and benchmarks.
A technical comparison of Numba and Cython for accelerating Python code, including benchmarks and analysis of JIT vs. compiled approaches.
