⚡️ Speed up function parse_wnid by 45% - #7
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The optimization replaces the inline regex pattern compilation with a pre-compiled regex pattern stored in a module-level constant `_WNID_RE = re.compile(r"n\d{8}")`.
**Key optimization**: Instead of recompiling the regex pattern `r"n\d{8}"` on every function call via `re.search()`, the pattern is compiled once at module import time and reused for all subsequent calls.
**Why this is faster**: Regex compilation is computationally expensive, involving pattern parsing, finite state machine construction, and optimization. The line profiler shows the regex search line dropped from 518,553ns to 156,941ns (70% reduction), explaining most of the 45% overall speedup.
**Performance characteristics**: The optimization provides consistent speedup across all test cases (28-76% faster), with particularly strong gains for:
- Simple exact matches (56-70% faster)
- Strings with early matches (65-76% faster)
- Large strings where the WNID appears early (58-64% faster)
The speedup is less pronounced for cases requiring extensive string scanning (like large strings with WNID at the end or many invalid patterns), but still meaningful at 4-19% improvement. This optimization is especially beneficial for workloads with frequent WNID parsing calls.
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📄 45% (0.45x) speedup for
parse_wnidinsrc/spdl/source/imagenet.py⏱️ Runtime :
161 microseconds→111 microseconds(best of283runs)📝 Explanation and details
The optimization replaces the inline regex pattern compilation with a pre-compiled regex pattern stored in a module-level constant
_WNID_RE = re.compile(r"n\d{8}").Key optimization: Instead of recompiling the regex pattern
r"n\d{8}"on every function call viare.search(), the pattern is compiled once at module import time and reused for all subsequent calls.Why this is faster: Regex compilation is computationally expensive, involving pattern parsing, finite state machine construction, and optimization. The line profiler shows the regex search line dropped from 518,553ns to 156,941ns (70% reduction), explaining most of the 45% overall speedup.
Performance characteristics: The optimization provides consistent speedup across all test cases (28-76% faster), with particularly strong gains for:
The speedup is less pronounced for cases requiring extensive string scanning (like large strings with WNID at the end or many invalid patterns), but still meaningful at 4-19% improvement. This optimization is especially beneficial for workloads with frequent WNID parsing calls.
✅ Correctness verification report:
🌀 Generated Regression Tests and Runtime
🔎 Concolic Coverage Tests and Runtime
codeflash_concolic_xpyvdxks/tmppai3f95x/test_concolic_coverage.py::test_parse_wnidTo edit these changes
git checkout codeflash/optimize-parse_wnid-mgqpw6yfand push.