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

TL;DR

This paper investigates optimizing the Fish School Behaviour algorithm for high-performance computing on the Setonix supercomputer using OpenMP, focusing on multi-threading strategies to improve computational efficiency.

Contribution

It provides a detailed analysis of parallel optimization techniques for FSB on Setonix with OpenMP, including thread management and scheduling strategies, offering insights for similar large-scale parallel algorithms.

Findings

Optimal thread counts improve performance significantly.

Scheduling strategies impact execution efficiency.

Insights applicable to other parallel algorithms using OpenMP.

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.