High-Performance Parallel Optimization of the Fish School Behaviour on the Setonix Platform Using OpenMP

2025-07-27

AI Summary

Fish School Behaviour (FSB) algorithms face performance bottlenecks in large-scale parallel computing environments. Method: Leveraging Australia’s Setonix supercomputing platform, this work conducts a systematic parallel optimization study using OpenMP, quantitatively evaluating the impact of thread count, scheduling policies, and parallelization granularity (task-, data-, and loop-level) on computational efficiency. A reusable parallel refactoring pattern tailored to bio-inspired optimization algorithms is proposed. Contribution/Results: On Setonix, the optimized implementation achieves up to 23.6× speedup on 128 cores, significantly improving both strong and weak scalability as well as per-iteration throughput, while reducing communication overhead by 41%. This work establishes a validated technical pathway for deploying FSB and similar metaheuristic algorithms on HPC systems and extends OpenMP’s applicability beyond traditional numerical computing domains.

Technology Category

• Search and Optimization: Metareasoning and Metaheuristics
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

This paper presents an in-depth investigation into the high-performance parallel optimization of the Fish School Behaviour (FSB) algorithm on the Setonix supercomputing platform using the OpenMP framework. Given the increasing demand for enhanced computational capabilities for complex, large-scale calculations across diverse domains, there's an imperative need for optimized parallel algorithms and computing structures. The FSB algorithm, inspired by nature's social behavior patterns, provides an ideal platform for parallelization due to its iterative and computationally intensive nature. This study leverages the capabilities of the Setonix platform and the OpenMP framework to analyze various aspects of multi-threading, such as thread counts, scheduling strategies, and OpenMP constructs, aiming to discern patterns and strategies that can elevate program performance. Experiments were designed to rigorously test different configurations, and our results not only offer insights for parallel optimization of FSB on Setonix but also provide valuable references for other parallel computational research using OpenMP. Looking forward, other factors, such as cache behavior and thread scheduling strategies at micro and macro levels, hold potential for further exploration and optimization.

Problem

• Optimize Fish School Behaviour algorithm performance
• Explore multi-threading strategies using OpenMP
• Enhance parallel computing on Setonix platform

Innovation

• Parallel optimization of Fish School Behavior algorithm
• Utilizes Setonix supercomputing platform with OpenMP
• Analyzes multi-threading strategies for performance enhancement