ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2018, Vol. 55 ›› Issue (4): 885-892.doi: 10.7544/issn1000-1239.2018.20160875

• 软件技术 • 上一篇    

最小通信开销的Direct Send并行图像合成方法


  1. (西北核技术研究所 西安 710024) (
  • 出版日期: 2018-04-01

A Direct Send Image Compositing Algorithm with Minimal Communication Costs

Wang Pan, Yang Pingli, Huang Shaohua, Lin Chengdi, Kong Longxing   

  1. (Northwest Institute of Nuclear Technology, Xi’an 710024)
  • Online: 2018-04-01

摘要: Sort-last并行绘制方法广泛应用于大规模科学数据的并行可视化过程,而并行图像合成方法直接决定了Sort-last方法的总体绘制性能.针对目前Direct Send图像合成方法中存在的不足,提出一种通信开销最小的Direct Send并行图像合成方法,该方法首先使用GPU多线程方式统计各个绘制节点图像的有效像素前缀和,再利用动态规划方法计算有效像素前缀和列表的最佳分割位置,使并行图像合成的通信开销最小.该方法改变了传统Direct Send方法中静态均匀分配图像子块的合成模式,实验表明:所提出的Direct Send方法在并行图像合成性能方面明显优于现有方法,为后续以Direct Send方法为基石构建更高效的大规模并行图像合成方法奠定了基础.

关键词: 并行可视化, 并行图像合成, 图像压缩, 最小通信开销

Abstract: Sort-last is the most widely used method for large scale parallel visualization, and the bottleneck of sort-last method is the image compositing stage. Direct Send is the cornerstone for all other compositing algorithms, so it makes a lot of sense to improve its performance for accelerating image composition. To minimize the communication cost in image compositing, we propose a new type of Direct Send method. Compared with the static partition strategy of the traditional Direct Send, our method is dynamic and adaptive, and it is composed of two phrases: Firstly, we compute all active pixel prefix sums of each image by GPU multi-threads in parallel. This process can remove the background pixels tremendously, and the images are all compressed efficiently. Secondly, a dynamic programming model is built and solved to generate the optimal partitions of subimages for Direct Send image compositing, which ensures that the communication costs of Direct Send is minimal. In the experiments, we firstly measure the image compression ratios of our method, and obtain the optimal size of pixel blocks. Then, we compare the compositing time of our method with RLE and greedy algorithm on varying number of rendering nodes, showing that our method is more efficient than the existing two methods.

Key words: parallel visualization, parallel image compositing, image compression, minimal communication cost