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Abstract: Python
is the dominant language in scientific computing thanks to its simplicity
and rich ecosystem\, but it often lags behind compiled languages in perfor
mance. This course explores practical techniques for accelerating Python w
orkflows\, including NumPy vectorization and just-in-time compilation with
Numba. We then cover parallel computing\, from multithreading and multipr
ocessing to distributed computing with Ray for scaling CPU- and I/O-bound
workloads. Finally\, we introduce GPU acceleration with Numba CUDA\, inclu
ding array operations\, custom kernels\, memory management\, and performan
ce tuning\, culminating in a GPU-based prime factorization project.