🐏 RamParILS
A parallel Rust rewrite of ParamILS — automated algorithm configuration via Iterated Local Search.
Used as the inner tuner in Grackle, a strategy portfolio invention system for automated reasoning solvers.
💡 What it does
Given a target algorithm with configurable parameters, RamParILS searches for the parameter setting that minimises runtime or a numeric solution cost on a set of training instances. It uses FocusedILS by default. RamParILS starts by scoring configurations on a configurable prefix of the training instances and increases that shared fidelity when the incumbent survives a challenge. Neighbours are submitted to a bounded worker pool, and the first fully evaluated improvement is accepted. Results can be stored in a persistent SQLite cache for reuse by compatible tuning runs.
Cache entries are keyed only by the active configuration and instance path. Use a separate cache when the algorithm command, cutoff, objective, solver version, wrapper behavior, or random seed changes. Reusing a cache across incompatible scenarios can silently return stale results.
🚀 Key differences from Ruby ParamILS
| Ruby ParamILS | RamParILS | |
|---|---|---|
| Evaluation | Sequential | Parallel over all (neighbor, instance) pairs |
| Cache | In-memory, per-run | Persistent SQLite, shared across runs |
| Python API | subprocess call | Native extension via PyO3 |
| Non-deterministic algorithms (multiple seeds) | Supported | Not (yet) supported |
Parallel evaluation is the primary motivation for the rewrite. Actual speedup depends on worker count, neighbourhood width, current fidelity, solver runtimes, early acceptance, and cache hits. The persistent cache compounds this advantage across compatible Grackle tuning runs on overlapping problem sets.
🔗 Quick links
🤝 Acknowledgements
This project is part of DEEPER and supported by the DEEPER grant from Renaissance Philanthropy.