摘 要
随着信息技术的迅猛发展，大数据已成为当今社会的重要资产，其规模和复杂度呈指数级增长，传统计算技术难以满足高效处理需求。为此，本研究聚焦于大数据环境下的并行计算技术，旨在探索适用于海量数据处理的新方法与新架构。通过对当前主流并行计算框架进行深入剖析，结合实际应用场景需求，提出了一种基于动态资源调度的优化算法，该算法能够根据任务特征自动调整计算资源分配，有效提高系统吞吐量。同时，针对现有技术在数据局部性和通信开销方面存在的问题，引入了分布式缓存机制与智能预取策略，显著降低了网络传输延迟。实验结果表明，在多个典型大数据处理任务中，所提方案较传统方法性能提升30%以上。此外，本研究还构建了一个通用型并行计算平台原型，支持多种编程模型无缝集成，为后续研究提供了坚实基础。本研究不仅丰富了并行计算理论体系，而且为解决实际工程中的大数据处理难题提供了有效途径，具有重要的学术价值和广阔的应用前景。
关键词：大数据处理 并行计算技术 动态资源调度 分布式缓存
Abstract
With the rapid development of information technology, big data has become a crucial asset in modern society, characterized by exponential growth in both scale and complexity. Traditional computing technologies struggle to meet the demands of efficient processing in such an environment. This study focuses on parallel computing techniques in big data environments, aiming to explore new methods and architectures for large-scale data processing. By conducting an in-depth analysis of mainstream parallel computing fr ameworks such as MapReduce and Spark, and considering practical application requirements, we propose an optimization algorithm based on dynamic resource scheduling. This algorithm automatically adjusts computational resource allocation according to task characteristics, thereby significantly enhancing system throughput. Addressing the issues of data locality and communication overhead in existing technologies, we introduce distributed caching mechanisms and intelligent prefetching strategies, which notably reduce network transmission latency. Experimental results demonstrate that our proposed solution achieves more than a 30% performance improvement over traditional methods in various typical big data processing tasks. Furthermore, this research develops a prototype of a general-purpose parallel computing platform that supports seamless integration of multiple programming models, providing a solid foundation for future studies. This work not only enriches the theoretical fr amework of parallel computing but also offers effective solutions to big data processing challenges in practical engineering applications, highlighting its significant academic value and broad application prospects.
Keyword:Big data processing Parallel computing technology Dynamic resource scheduling Distributed cache
目 录
1 引言 1
2 大数据处理框架分析 1
2.1 常见大数据处理平台 1
2.2 分布式文件系统研究 2
2.3 数据存储与管理技术 2
3 并行计算模型探讨 3
3.1 主流并行计算模型 3
3.2 模型选择与优化策略 4
3.3 实际应用案例分析 4
4 性能评估与优化方法 5
4.1 性能评估指标体系 5
4.2 瓶颈问题识别方法 6
4.3 优化算法与实践 6
5 结论 7
参考文献 8
致谢 9
