Parallel Determinism
SkillDev toolsProvides patterns and utilities to ensure parallel TF-IDF results are identical to the sequential baseline, addressing floating-point ordering, tie-breaking, and pickling compatibility.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Parallel Determinism skill
What this skill tells your AI
The instructions your AI receives, as published by openlair/openskill in tasks-evolved/parallel-tfidf-search/environment/skills/evo-parallel-determinism/SKILL.md and read by ahel’s review.
Overview
Ensures parallel results match sequential exactly by handling:
- Floating-point accumulation order
- Tie-breaking in result sorting
- Pickling compatibility for multiprocessing
- Safe context selection (fork vs spawn)
Key Principles
- FP Ordering: Iterate query terms in same order as sequential (dict insertion order)
- Tie-breaking: Sort by (-score, doc_id) for deterministic ordering
- Pickling: Define NamedTuples/dataclasses at module level
- Context: Use 'fork' on Linux for performance (copy-on-write)
Usage
import sys
sys.path.insert(0, '/app/environment/skills/evo-parallel-determinism/scripts')
from utils import (
deterministic_score_accumulation,
stable_sort_results,
get_safe_mp_context
)
Functions
deterministic_score_accumulation(query_vector, doc_vector)- FP-safe dot productstable_sort_results(results)- Sort with deterministic tie-breakingget_safe_mp_context()- Get appropriate multiprocessing context
Signals
- GitHub stars
- 89
- Forks
- 4
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
evo-parallel-determinism- Source
- github.com/openlair/openskill