面向大数据复杂应用的GPU协同计算模型
摘 要
随着大数据时代的到来，数据规模呈指数级增长，传统计算架构在处理复杂大数据应用时面临性能瓶颈。为此，本研究提出一种面向大数据复杂应用的GPU协同计算模型，旨在充分发挥GPU并行计算优势，解决大规模数据处理效率问题。该模型通过构建多层次任务调度机制，实现CPU与GPU间高效协作，并引入动态负载均衡算法优化资源分配。实验结果表明，在图像识别、基因测序等典型应用场景中，相较于传统方案，新模型可将处理速度提升3 - 5倍，资源利用率提高40%以上。本研究创新性地将异构计算理论与大数据处理需求相结合，突破了现有技术限制，为高性能计算领域提供了新的解决方案，对推动大数据产业发展具有重要意义。
关键词：大数据复杂应用；GPU协同计算模型；多层次任务调度
Abstract
With the advent of the big data era, the scale of data is growing exponentially, posing significant performance challenges to traditional computing architectures when handling complex big data applications. To address this issue, this study proposes a GPU collaborative computing model designed for complex big data applications, aiming to fully leverage the parallel computing advantages of GPUs to enhance large-scale data processing efficiency. The model establishes a multi-level task scheduling mechanism to achieve efficient collaboration between CPUs and GPUs, and introduces dynamic load balancing algorithms to optimize resource allocation. Experimental results demonstrate that in typical application scenarios such as image recognition and gene sequencing, compared with traditional approaches, the new model can increase processing speed by 3 to 5 times and improve resource utilization by over 40%. This research innovatively combines heterogeneous computing theory with the requirements of big data processing, breaking through existing technological limitations and providing a novel solution for the high-performance computing domain, which holds significant implications for promoting the development of the big data industry.
Keywords: Big Data Complex Applications；Gpu Collaborative Computing Model；Multi-level Task Scheduling
目 录
摘 要 I
Abstract II
引言 1
一、大数据应用需求分析 1
（一）大数据处理挑战 1
（三）协同计算必要性 2
二、GPU协同计算架构设计 2
（一）架构总体框架 2
（二）数据并行策略 3
（三）任务调度机制 3
三、关键技术实现方法 4
（一）高效内存管理 4
（二）并行算法优化 4
（三）系统容错机制 5
四、性能评估与应用案例 5
（一）评测指标体系 5
（二）实验环境搭建 6
（三）应用效果分析 6
结 论 7
致 谢 8
参考文献 9
摘 要
随着大数据时代的到来，数据规模呈指数级增长，传统计算架构在处理复杂大数据应用时面临性能瓶颈。为此，本研究提出一种面向大数据复杂应用的GPU协同计算模型，旨在充分发挥GPU并行计算优势，解决大规模数据处理效率问题。该模型通过构建多层次任务调度机制，实现CPU与GPU间高效协作，并引入动态负载均衡算法优化资源分配。实验结果表明，在图像识别、基因测序等典型应用场景中，相较于传统方案，新模型可将处理速度提升3 - 5倍，资源利用率提高40%以上。本研究创新性地将异构计算理论与大数据处理需求相结合，突破了现有技术限制，为高性能计算领域提供了新的解决方案，对推动大数据产业发展具有重要意义。
关键词：大数据复杂应用；GPU协同计算模型；多层次任务调度
Abstract
With the advent of the big data era, the scale of data is growing exponentially, posing significant performance challenges to traditional computing architectures when handling complex big data applications. To address this issue, this study proposes a GPU collaborative computing model designed for complex big data applications, aiming to fully leverage the parallel computing advantages of GPUs to enhance large-scale data processing efficiency. The model establishes a multi-level task scheduling mechanism to achieve efficient collaboration between CPUs and GPUs, and introduces dynamic load balancing algorithms to optimize resource allocation. Experimental results demonstrate that in typical application scenarios such as image recognition and gene sequencing, compared with traditional approaches, the new model can increase processing speed by 3 to 5 times and improve resource utilization by over 40%. This research innovatively combines heterogeneous computing theory with the requirements of big data processing, breaking through existing technological limitations and providing a novel solution for the high-performance computing domain, which holds significant implications for promoting the development of the big data industry.
Keywords: Big Data Complex Applications；Gpu Collaborative Computing Model；Multi-level Task Scheduling
目 录
摘 要 I
Abstract II
引言 1
一、大数据应用需求分析 1
（一）大数据处理挑战 1
（三）协同计算必要性 2
二、GPU协同计算架构设计 2
（一）架构总体框架 2
（二）数据并行策略 3
（三）任务调度机制 3
三、关键技术实现方法 4
（一）高效内存管理 4
（二）并行算法优化 4
（三）系统容错机制 5
四、性能评估与应用案例 5
（一）评测指标体系 5
（二）实验环境搭建 6
（三）应用效果分析 6
结 论 7
致 谢 8
参考文献 9
