Your Escape Plan From Numpy + Cython
summary
Custom one‑dimensional logsumexp benchmark compares pure NumPy, SciPy, CuPy, Numba and Pythran on real data and reports concrete timings: SciPy ~6.7 s, CuPy 1.6 s (about 5× faster), Pythran 5.45 s (about 20–25% faster than SciPy). Numba gives mixed results: njit ~6.0 s, object‑mode jit ~6.7 s, so speedups are not guaranteed. Code changes and data movement matter: minimal NumPy rewrites, GPU transfer cost, and whether a function subset is supported determine which tool actually improves performance.
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chapters (7)
- 0:00Introduction
- 2:26Why not C
- 4:29Underflow
- 8:30Log Sum Exponential
- 9:37Coupon
- 15:17Python
- 21:17Decision Tree
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