基于GPU多流并发并行模型的NDVI提取算法

利用GPU进行加速的归一化差分植被指数(Normalized Differential Vegetation Index,NDVI)提取算法通常采用GPU多线程并行模型,存在弱相关计算之间以及CPU与GPU之间数据传输耗时较多等问题,影响了加速效果的进一步提升。针对上述问题,根据NDVI提取算法的特性,文中提出了一种基于GPU多流并发并行模型的NDVI提取算法。通过CUDA流和Hyper-Q特性,GPU多流并发并行模型可以使数据传输与弱相关计算、弱相关计算与弱相关计算之间达到重叠,从而进一步提高算法并行度及GPU资源利用率。文中首先通过GPU多线程并行模型对NDVI提取算法进行优化,并对优化后的计算过程进行分解,找出包含数据传输及弱相关性计算的部分;其次,对数据传输和弱相关计算部分进行重构,并利用GPU多流并发并行模型进行优化,使弱相关计算之间、弱相关计算和数据传输之间达到重叠的效果;最后,以高分一号卫星拍摄的遥感影像作为实验数据,对两种基于GPU实现的NDVI提取算法进行实验验证。实验结果表明,与传统基于GPU多线程并行模型的NDVI提取算法相比,所提算法在影像大于12000*12000像素时平均取得了约1.5倍的加速,与串行提取算法相比取得了约260倍的加速,具有更好的加速效果和并行性。

Extraction Algorithm of NDVI Based on GPU Multi-stream Parallel Model

In general,the Normalized Differential Vegetation Index(NDVI)extraction algorithm optimized by GPU usually adopts GPU multi-thread parallel model,and there are problems such as data transmission between CPU and GPU and weak correlation calculations taking more time,which affect the further improvement of performance.Aiming at the above problems and the characteristics of NDVI,a NDVI extraction algorithm based on GPU multi-stream parallel model was proposed.Through the features of CUDA stream and Hyper-Q,the GPU multi-stream parallel model can overlap not only data transmission and kernel execution,but also kernel execution and kernel execution,and further improve parallelism and resources utilization of GPU.Firstly,the NDVI algorithm is optimized by the GPU multi-thread parallel model,and the optimized procedures are decomposed to find out the parts of the algorithm with data transmission or weak correlation calculation.Secondly,parts of data transmission and weak correlation calculation are reconstructed and optimized by GPU multi-stream parallel model to achieve overlapping between weak correlation calculation and weak correlation calculation,or weak correlation calculation and data transmission.Finally,expe-riments of NDVI algorithm that based on both GPU parallel models respectively were carried out,and the remote sensing image taken by the GF1 satellite were used as experimental data.The experimental results show that the proposed algorithm,when the image is larger than 12000x13400 pixels,achieves about 1.5 times acceleration compared with the traditional parallel NDVI algorithm based on the GPU multi-thread parallel model,and about 260 times acceleration compared with the NDVI sequential extraction algorithm,which has better performance and parallelism.