用OpenMP在超算上优化鱼群算法，提升并行效率。
High-Performance Parallel Optimization of the Fish School Behaviour on the Setonix Platform Using OpenMP
- 采用OpenMP在Setonix平台对鱼群行为算法进行多线程优化。
- 通过调整线程数与调度策略，显著提升算法运行速度。
- 适合高性能计算与并行算法研究者参考。
本文深入研究了在Setonix超级计算平台上，基于OpenMP框架对鱼群行为（Fish School Behaviour, FSB）算法进行高性能并行优化。随着各领域对复杂大规模计算需求的增长，优化并行算法与计算架构变得尤为迫切。FSB算法因其迭代性强、计算密集，天然适合并行化。本研究利用Setonix平台和OpenMP框架，分析线程数量、调度策略及OpenMP构造等多线程因素，探索提升程序性能的规律与策略。实验设计严谨，验证了多种配置下的表现，不仅为FSB在Setonix上的并行优化提供洞见，也为其他基于OpenMP的并行计算研究提供重要参考。未来可进一步探索缓存行为及微观与宏观层面的线程调度策略。
原文摘要 · Abstract (English)
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.
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