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cuRPQ: A High-Performance GPU-Based Framework for Processing Regular and Conjunctive Regular Path Queries

Sungwoo Park, Seohyeon Kim, Min-Soo Kim

Abstract

Regular path queries (RPQs) are fundamental for path-constrained reachability analysis, and more complex variants such as conjunctive regular path queries (CRPQs) are increasingly used in graph analytics. Evaluating these queries is computationally expensive, but to the best of our knowledge, no prior work has explored GPU acceleration. In this paper, we propose cuRPQ, a high-performance GPU-optimized framework for processing RPQs and CRPQs. cuRPQ addresses the key GPU challenges through a novel traversal algorithm, an efficient visited-set management scheme, and a concurrent exploration-materialization strategy. Extensive experiments show that cuRPQ outperforms state-of-the-art methods by orders of magnitude, without out-of-memory errors.

cuRPQ: A High-Performance GPU-Based Framework for Processing Regular and Conjunctive Regular Path Queries

Abstract

Regular path queries (RPQs) are fundamental for path-constrained reachability analysis, and more complex variants such as conjunctive regular path queries (CRPQs) are increasingly used in graph analytics. Evaluating these queries is computationally expensive, but to the best of our knowledge, no prior work has explored GPU acceleration. In this paper, we propose cuRPQ, a high-performance GPU-optimized framework for processing RPQs and CRPQs. cuRPQ addresses the key GPU challenges through a novel traversal algorithm, an efficient visited-set management scheme, and a concurrent exploration-materialization strategy. Extensive experiments show that cuRPQ outperforms state-of-the-art methods by orders of magnitude, without out-of-memory errors.
Paper Structure (26 sections, 18 figures, 8 tables, 1 algorithm)

This paper contains 26 sections, 18 figures, 8 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Preliminaries
  3. Problem Definition
  4. Processing RPQs
  5. WavePlan
  6. Labeled Grid Format
  7. Challenges of GPU-Based CRPQ Processing
  8. GPU-Based Hop-Limited Level-Wise DFS
  9. Base Phase
  10. Expansion Phase
  11. GPU-Based On-Demand Segment Pooling
  12. Visited Segment
  13. Checkpoint Segment
  14. Bridge Segment
  15. Concurrent Exploration-Materialization
  16. ...and 11 more sections

Figures (18)

  • Figure 1: Example of RPQ and CRPQ on a data graph.
  • Figure 2: Example of automata-based plan and product graph.
  • Figure 3: Example of WavePlans.
  • Figure 4: Example of Labeled Grid Format (LGF).
  • Figure 5: Example of hop-limited level-wise DFS.
  • ...and 13 more figures

Theorems & Definitions (3)

  • definition 1
  • definition 2
  • definition 3