基于GPU的高性能计算优化技术研究
随着高性能计算需求的日益增长，传统CPU架构在处理大规模并行计算任务时逐渐显现出性能瓶颈。基于GPU的高性能计算优化技术成为突破这一瓶颈的关键。本研究旨在探索GPU架构下计算资源的高效利用方法，通过深入分析GPU硬件特性与并行算法设计之间的关系，提出了一种面向通用计算任务的GPU优化框架。该框架结合了线程调度、内存访问模式优化以及计算密集型任务映射等关键技术，有效提升了计算效率。实验结果表明，在典型科学计算场景中，所提出的优化方案能够实现平均3.5倍以上的性能提升，部分复杂计算任务加速比可达8倍以上。此外，本研究还针对GPU异构编程模型进行了系统性改进，提出了动态负载均衡机制和自适应任务分配策略，显著降低了上下文切换开销。
关键词：GPU优化框架 并行计算任务 异构编程模型
Abstract
With the increasing demand for high-performance computing, traditional CPU architectures are gradually showing performance bottlenecks when handling massively parallel computing tasks. High-performance computing optimization technology based on GPU becomes the key to break through this bottleneck. This study aims to explore the efficient utilization method of computing resources under GPU architecture, and propose a GPU optimization fr amework for general computing tasks by deeply analyzing the relationship between GPU hardware features and parallel algorithm design. The proposed fr amework combines the key technologies, such as thread scheduling, memory access mode optimization, and computation-intensive task mapping, to effectively improve the computational efficiency. The experimental results show that in the typical scientific computing scenario, the proposed optimization scheme can achieve an average performance improvement of more than 3.5 times, and some complex computing tasks can accelerate by more than 8 times. In addition, this study also makes systematic improvements for the GPU heterogeneous programming model, proposing a dynamic load balancing mechanism and an adaptive task assignment strategy, which significantly reduces the context switching overhead.
Keyword:Gpu Optimization fr amework Parallel Computing Tasks Thread Scheduling And Memory Access Optimization
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法与技术路线 2
2GPU架构与性能分析 2
2.1性能评估指标体系 2
2.2 GPU基本架构原理 3
3高性能计算优化策略 3
3.1并行计算模型优化 3
3.2内存访问模式优化 4
3.3任务调度与负载均衡 5
4实验验证与结果分析 5
4.1实验平台搭建 5
4.2优化效果评估 6
4.3结果对比与讨论 7
结论 7
参考文献 9
致谢 10
摘 要
随着高性能计算需求的日益增长，传统CPU架构在处理大规模并行计算任务时逐渐显现出性能瓶颈。基于GPU的高性能计算优化技术成为突破这一瓶颈的关键。本研究旨在探索GPU架构下计算资源的高效利用方法，通过深入分析GPU硬件特性与并行算法设计之间的关系，提出了一种面向通用计算任务的GPU优化框架。该框架结合了线程调度、内存访问模式优化以及计算密集型任务映射等关键技术，有效提升了计算效率。实验结果表明，在典型科学计算场景中，所提出的优化方案能够实现平均3.5倍以上的性能提升，部分复杂计算任务加速比可达8倍以上。此外，本研究还针对GPU异构编程模型进行了系统性改进，提出了动态负载均衡机制和自适应任务分配策略，显著降低了上下文切换开销。
关键词：GPU优化框架 并行计算任务 异构编程模型
Abstract
With the increasing demand for high-performance computing, traditional CPU architectures are gradually showing performance bottlenecks when handling massively parallel computing tasks. High-performance computing optimization technology based on GPU becomes the key to break through this bottleneck. This study aims to explore the efficient utilization method of computing resources under GPU architecture, and propose a GPU optimization fr amework for general computing tasks by deeply analyzing the relationship between GPU hardware features and parallel algorithm design. The proposed fr amework combines the key technologies, such as thread scheduling, memory access mode optimization, and computation-intensive task mapping, to effectively improve the computational efficiency. The experimental results show that in the typical scientific computing scenario, the proposed optimization scheme can achieve an average performance improvement of more than 3.5 times, and some complex computing tasks can accelerate by more than 8 times. In addition, this study also makes systematic improvements for the GPU heterogeneous programming model, proposing a dynamic load balancing mechanism and an adaptive task assignment strategy, which significantly reduces the context switching overhead.
Keyword:Gpu Optimization fr amework Parallel Computing Tasks Thread Scheduling And Memory Access Optimization
目 录
1绪论 1
1.1研究背景与意义 1
1.2国内外研究现状 1
1.3研究方法与技术路线 2
2GPU架构与性能分析 2
2.1性能评估指标体系 2
2.2 GPU基本架构原理 3
3高性能计算优化策略 3
3.1并行计算模型优化 3
3.2内存访问模式优化 4
3.3任务调度与负载均衡 5
4实验验证与结果分析 5
4.1实验平台搭建 5
4.2优化效果评估 6
4.3结果对比与讨论 7
结论 7
参考文献 9
致谢 10
