⚡ Bolt: [performance improvement] Regex fast-paths and string hashing optimization#44
⚡ Bolt: [performance improvement] Regex fast-paths and string hashing optimization#44alinelena wants to merge 1 commit into
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…tring hashing - Implemented fast-path literal string pre-checks in `parse_dipole`, `parse_quadrupole`, `parse_charge_mult`, and `parse_eigens` to avoid executing expensive regular expressions on large text blocks when no relevant keywords are present. - Optimized `geom_sha1` in `process_omol25.py` by replacing an iterative `.update()` loop with a `"".join(...)` generator expression, reducing Python function call overhead. - Verified optimizations with unit tests. Co-authored-by: alinelena <3306823+alinelena@users.noreply.github.com>
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💡 What:
Added literal string fast-path checks (e.g.,
if "Dipole" not in txt...) before executing complex regular expressions in parsing functions withinsrc/lavello_mlips/process_omol25.py. I also refactored thegeom_sha1function to use a"".join(...)generator instead of repeatedly calling.update()inside a loop.🎯 Why:
Running the C-optimized Regex engine is fast, but invoking it thousands of times on massive string blocks when no match exists is pure overhead. Bypassing the regex engine entirely when target keywords are clearly absent significantly speeds up parsing. For the
geom_sha1fingerprinting, iterative function calls likeh.update()inside loops create noticeable Python overhead; building a single string and calling it once is more performant.📊 Impact:
Based on isolated benchmarking:
"".join(...)for string hashing is slightly faster (reduces loop overhead) than iterative updates.🔬 Measurement:
Run the core test suite to ensure functional parity and no regressions:
python -m pytest -k "not mpi" tests/PR created automatically by Jules for task 14961935722466142427 started by @alinelena