摘 要
随着信息技术的迅猛发展，大数据处理成为计算机科学领域的研究热点，传统快速排序算法在处理大规模数据集时面临效率低下、内存占用高等问题。为此，本研究提出一种改进型快速排序算法，旨在优化大数据集排序性能。该算法通过引入自适应划分策略和多线程并行处理机制，在保证算法稳定性的前提下显著提升排序效率。实验采用多种规模的真实数据集进行测试，利用时间复杂度、空间复杂度以及实际运行时间作为评价指标。结果表明，改进后的算法在处理百万级乃至千万级数据量时，平均运行时间较经典快速排序降低约35%，内存消耗减少20%左右。此外，该算法具备良好的可扩展性，能够根据硬件配置动态调整线程数量，进一步提高排序效率。本研究不仅为解决大数据排序难题提供了有效方案，而且其创新性的自适应划分与并行处理相结合的思想对其他相关算法设计具有重要参考价值，为后续研究奠定了理论基础。
关键词：大数据排序；快速排序算法改进；自适应划分策略；多线程并行处理；算法性能优化
Abstract
With the rapid development of information technology, big data processing has become a research hotspot in the field of computer science. Traditional quicksort algorithms face issues such as low efficiency and high memory consumption when handling large-scale datasets. To address these challenges, this study proposes an improved quicksort algorithm aimed at optimizing sorting performance for big data sets. By introducing an adaptive partitioning strategy and a multi-threaded parallel processing mechanism, the algorithm significantly enhances sorting efficiency while maintaining stability. Experiments were conducted using various real-world datasets of different scales, evaluating the algorithm based on time complexity, space complexity, and actual runtime. The results demonstrate that the improved algorithm reduces average running time by approximately 35% and memory consumption by around 20% when processing datasets ranging from millions to tens of millions of records. Moreover, the algorithm exhibits excellent scalability, dynamically adjusting thread numbers according to hardware configurations to further improve sorting efficiency. This research not only provides an effective solution to the problem of big data sorting but also offers valuable insights into the combination of adaptive partitioning and parallel processing, which can serve as a reference for the design of other related algorithms and lay a theoretical foundation for future studies.
Keywords：Big Data Sorting；Improved Quick Sort Algorithm；Adaptive Partitioning Strategy；Multi-thread Parallel Processing；Algorithm Performance Optimization
目 录
摘 要 I
Abstract II
引 言 1
第一章 改进型快速排序算法概述 2
1.1 快速排序算法原理 2
1.2 改进型算法特点 2
1.3 改进的必要性分析 3
第二章 大数据集特性对排序的影响 5
2.1 数据规模与分布 5
2.2 内存与外存访问 5
2.3 数据局部性影响 6
第三章 改进型算法性能评估方法 8
3.1 性能评估指标 8
3.2 测试环境搭建 8
3.3 实验数据选取 9
第四章 性能对比与结果分析 11
4.1 传统与改进算法对比 11
4.2 不同数据集上的表现 11
4.3 算法优化效果总结 12
结 论 13
参考文献 14
致 谢 15
