A Parallel CPU-GPU Framework for Batching Heuristic Operations in Depth-First Heuristic Search

TL;DR

This paper introduces a CPU-GPU parallel framework for batching heuristic computations in depth-first search algorithms, significantly improving performance on complex puzzles like Rubik's Cube and sliding tile puzzles.

Contribution

It presents a novel parallelization approach for depth-first heuristics, combining CPU tree search with GPU heuristic evaluation, applicable to IDA* and BTS.

Findings

Significant speedup in puzzle solving times.

Effective batching of heuristic evaluations on GPU.

Applicable to various heuristic types and search problems.

Abstract

The rapid advancement of GPU technology has unlocked powerful parallel processing capabilities, creating new opportunities to enhance classic search algorithms. This hardware has been exploited in best-first search algorithms with neural network-based heuristics by creating batched versions of A* and Weighted A* that delay heuristic evaluation until sufficiently many states can be evaluated in parallel on the GPU. But, research has not addressed how depth-first algorithms like IDA* or Budgeted Tree Search (BTS) can have their heuristic computations batched. This is more complicated in a tree search, because progress in the search tree is blocked until heuristic evaluations are complete. In this paper we show that GPU parallelization of heuristics can be effectively performed when the tree search is parallelized on the CPU while heuristic evaluations are parallelized on the GPU. We develop a parallelized cost-bounded depth-first search (CB-DFS) framework that can be applied to both IDA* and BTS, significantly improving their performance. We demonstrate the strength of the approach on the 3x3 Rubik's Cube and the 4x4 sliding tile puzzle (STP) with both classifier-based and regression-based heuristics.