Takumi Shimoda, Alex Fukunaga

Decoupling Generation and Evaluation for Parallel Greedy Best-First Search

Introduction

Decoupling generation and evaluation for parallel greedy best-first search. Enhance parallel greedy best-first search by decoupling state generation & evaluation. Overcome costly constraint enforcement, boost state evaluation rate, and improve search performance.

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.

Review

This paper tackles a pertinent challenge in the domain of parallel search, specifically concerning constrained parallel greedy best-first search algorithms. These algorithms, while offering enhanced control over search behavior, are often plagued by inefficiencies due to threads waiting idly for states that satisfy specific expansion constraints. The authors adeptly identify this performance bottleneck as a critical area for improvement, establishing a clear motivation for their proposed work within the context of recent advancements in parallel search strategies. To address the aforementioned inefficiencies, the authors propose an innovative improvement centered on decoupling state generation from state evaluation. This architectural separation aims to alleviate the performance overhead associated with enforcing expansion constraints by allowing these two critical processes to operate more independently. The core hypothesis is that this decoupling will significantly improve the rate at which states are evaluated, thereby translating directly into superior overall search performance for constrained parallel greedy best-first search. The proposed decoupling strategy presents a promising avenue for enhancing the scalability and efficiency of parallel search algorithms, particularly in applications where complex constraints are necessary for guiding the search. By directly confronting the issue of idle threads and costly constraint enforcement, this work offers a valuable contribution towards optimizing parallel search performance. Should the empirical results substantiate the claimed improvements in state evaluation rate and overall search performance, this approach could significantly influence the design of future parallel search systems, making them more robust and practical.

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