摘 要
随着信息技术的迅猛发展，高性能计算在科学工程计算、大数据处理等领域发挥着不可替代的作用，而并行算法是提升高性能计算效率的关键。本研究旨在探索适用于不同应用场景的高效并行算法。针对传统并行算法存在的资源利用率低、可扩展性差等问题，提出一种基于动态负载均衡的新型并行算法框架。该框架通过实时监测任务执行状态，智能分配计算资源，有效提高资源利用率和算法可扩展性。以矩阵运算、图像处理等典型计算密集型任务为研究对象，采用MPI与OpenMP混合编程模型实现算法，在多核处理器集群环境下进行实验测试。结果表明，所提算法相较于传统方法，在处理大规模数据时性能提升显著，加速比接近理想值且具有良好的强弱可扩展性。这一成果不仅为解决现有并行算法面临的挑战提供新思路，还为推动高性能计算技术向更高效、更智能方向发展作出重要贡献。
关键词：高性能计算 并行算法 动态负载均衡
Abstract
With the rapid development of information technology, high-performance computing plays an indispensable role in scientific and engineering computation, big data processing, and other fields, while parallel algorithms are critical to enhancing the efficiency of high-performance computing. This study aims to explore efficient parallel algorithms suitable for different application scenarios. Addressing the issues of low resource utilization and poor scalability in traditional parallel algorithms, a novel parallel algorithm fr amework based on dynamic load balancing is proposed. This fr amework improves resource utilization and algorithm scalability by monitoring task execution status in real-time and intelligently allocating computational resources. Using typical compute-intensive tasks such as matrix operations and image processing as research subjects, the algorithm is implemented using a hybrid MPI and OpenMP programming model and tested in a multi-core processor cluster environment. The results show that, compared with traditional methods, the proposed algorithm significantly improves performance when handling large-scale data, achieving near-ideal speedup ratios and demonstrating good strong and weak scalability. This achievement not only provides new insights into addressing the challenges faced by existing parallel algorithms but also makes significant contributions to advancing high-performance computing technology towards more efficient and intelligent directions.
Keyword:High-Performance Computing Parallel Algorithm Dynamic Load Balancing
目 录
引言 1
1并行算法基础理论 1
1.1并行计算模型分析 1
1.2并行算法设计原则 2
1.3并行算法性能评估方法 2
2并行算法优化技术 3
2.1任务划分与负载均衡 3
2.2数据通信优化策略 3
2.3内存访问模式优化 4
3典型并行算法研究 4
3.1矩阵运算并行算法 4
3.2图算法的并行实现 5
3.3科学计算中的并行算法 6
4并行算法应用案例 6
4.1大数据分析中的并行算法 6
4.2深度学习训练并行化 7
4.3气象模拟并行算法应用 7
结论 8
参考文献 9
致谢 9
