Add performance analysis report for Python/GPU implementation#1
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…is report Agent-Logs-Url: https://github.com/mfournigault/Handheld-Multi-Frame-Super-Resolution/sessions/5adf1637-65f1-405d-a55b-1ccce7e007a4 Co-authored-by: mfournigault <4528398+mfournigault@users.noreply.github.com>
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Adds
analyse_problemes_performance.md, a detailed French-language technical report analysing why this Python/Numba+PyTorch implementation is significantly slower than Google's original mobile ISP pipeline.Report covers
12 identified bottlenecks with precise file/line references, including:
cuda.synchronize()barriers per burst from PyTorch/Numba framework mixing (alignment.py:107)super_resolution.py:144)process()call (super_resolution.py:254)block_matching.py:167, identical in 32/64 variants)utils_image.py:303)DEFAULT_THREADS=16)Prioritised fix recommendations: quick wins (cache, constant kernel hoisting, thread config), medium-term (stream overlap, single-backend migration, AOT compilation), and structural refactors
Estimated impact: quick wins + medium-term optimisations projected to bring a 12MP×20-frame burst from ~4s to <1.5s on RTX 3090, plus eliminating JIT cold-start entirely