摘要
随着计算机技术的迅猛发展，并行计算成为提升计算性能的关键途径，图形处理器（GPU）凭借其强大的并行处理能力在并行计算领域展现出巨大潜力。本研究旨在探索GPU加速技术在并行计算中的应用，以解决传统CPU计算资源有限、计算效率低的问题。通过分析GPU架构特点与并行算法需求的匹配性，采用CUDA编程模型实现典型并行算法在GPU上的移植优化，针对矩阵运算、图像处理等计算密集型任务进行实验测试。结果表明，相较于传统CPU串行计算方式，基于GPU加速技术可使计算速度显著提升，部分算法加速比可达数十倍以上。该研究创新性地提出一种适用于GPU架构的混合并行策略，有效解决了数据传输瓶颈问题，提高了整体计算效率。此外，还构建了通用的GPU加速框架，为其他类似并行计算任务提供了参考方案，对推动GPU在更多领域广泛应用具有重要意义。
关键词：GPU加速技术；并行计算；CUDA编程模型
Abstract
With the rapid advancement of computer technology, parallel computing has become a critical approach to enhancing computational performance. Graphics Processing Units (GPU), owing to their superior parallel processing capabilities, have demonstrated significant potential in the field of parallel computing. This study aims to explore the application of GPU acceleration technology in parallel computing to address the limitations of traditional CPU-based computing resources and low computational efficiency. By analyzing the compatibility between GPU architecture characteristics and the requirements of parallel algorithms, this research implements the optimization of typical parallel algorithms on GPUs using the CUDA programming model. Experimental tests were conducted on compute-intensive tasks such as matrix operations and image processing. The results indicate that compared with traditional CPU serial computing methods, GPU-accelerated technology can significantly improve computational speed, with some algorithms achieving speedups of over tenfold. This study innovatively proposes a hybrid parallel strategy suitable for GPU architecture, effectively addressing data transfer bottlenecks and improving overall computational efficiency. Additionally, a general GPU acceleration fr amework was constructed, providing a reference solution for similar parallel computing tasks and contributing significantly to the broader application of GPUs across various domains.
Keywords：Gpu Acceleration Technology; Parallel Computing; Cuda Programming Model; Matrix Operations
目 录
摘要 I
Abstract II
一、绪论 1
(一) 图形处理器加速技术的背景与意义 1
(二) 并行计算领域研究现状综述 1
二、图形处理器架构与并行计算原理 2
(一) 并行计算模型及其优化 2
(二) 架构对性能的影响因素 2
三、图形处理器在科学计算中的应用 3
(一) 科学计算任务特征分析 3
(二) 性能评估与优化策略 4
(三) 应用前景与发展方向 5
四、图形处理器在大数据处理中的应用 5
(一) 大数据处理需求与挑战 5
(二) 实时数据分析性能提升 6
(三) 面向未来的优化方向 7
结 论 8
参考文献 9
