Decoupling Generation and Evaluation for Parallel Greedy Best-First Search(extended version)

TL;DR

This paper introduces a method to decouple state generation and evaluation in parallel greedy best-first search, significantly improving evaluation rate and overall search efficiency by reducing idle waiting times.

Contribution

It presents a novel approach to improve constrained parallel search by decoupling generation and evaluation, enhancing performance over existing methods.

Findings

Decoupling increases state evaluation rate.

Improved search efficiency demonstrated.

Reduces idle waiting in parallel search.

Abstract

In order to understand and control the search behavior of parallel search, recent work has proposed a class of constrained parallel greedy best-first search algorithms which only expands states that satisfy some constraint.However, enforcing such constraints can be costly, as threads must be waiting idly until a state that satisfies the expansion constraint is available. We propose an improvement to constrained parallel search which decouples state generation and state evaluation and significantly improves state evaluation rate, resulting in better search performance.