高性能CPU/GPU协同并行可视化技术研究

项目名称： 高性能CPU/GPU协同并行可视化技术研究

项目编号： No.61272009

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 曾亮

作者单位： 中国人民解放军国防科学技术大学

项目金额： 60万元

中文摘要： 多CPU多GPU体系结构的异构计算平台是目前高性能计算领域研究的热点问题，而GPU强大的通用计算能力以及图形绘制能力正好为高性能计算可视化领域提供了发展契机。本项目拟以研究多CPU多GPU混合异构计算模式为重点，深入分析当前多CPU多GPU系统的主要计算模式，并结合海量数据可视化应用需求特点，提出适合高性能可视计算领域的并行可视化技术和方法，重点解决关键问题(1)节点内多GPU分布显存模式下，同一窗口多线程高效并行绘制方法；(2)节点内多CPU多GPU间数据预存取管理方法；(3)节点内多CPU多GPU并行可视化算法存储优化与任务调度算法；(4)节点内多CPU多GPU协同混合加速并行绘制算法；(5)GPU集群环境下，面向海量数据可视化应用的并行绘制算法；(6)面向多CPU多GPU并行算法设计平台与并行绘制平台。本课题相关研究是高性能可视化及并行绘制领域重要基础创新研究，相关成果具有重要意义。

中文关键词： 科学计算可视化；并行绘制算法；多CPU与多GPU异构协同计算；图像合成算法；

英文摘要： Due to its high performance/cost ratio, the computing platform equipped with multi-GPU and many-CPU has been widely used in many application domain.The powerful computing ability of GPGPU give us an opportunity to improve the current performance of visualization and rendering. In this project, we will take the point of researching CPU-GPU cooperation computing and rendering as our main task, and will deeply research the architecture of the computing mode of CPU/GPU. In order to proving our research, we explore the requirements of large scale dataset science visualization, and combine it as our application domain. The final objective of our work is to dramatically improve the performance of high-performance visualization computer clusters. These are the key topics of our research: (1) Multi-GPU Parallel rendering for the same window in a single PC; (2) The data prefecth and transfer method in Multi-GPU and many-CPU core architecture; (3) The data storing and loading strategy of the classic parallel visualization algorithm; (4)CPU-GPU cooperation computing and rendering approaches in a single PC; (5) Large scale data set visualization algorithm for GPU clusters; (6) The multi-GPU and many-CPU orientied parallel rendering and visualization platform. This research of our project is an important application for HPC

英文关键词： Scientific Computing Visualization；Parallel Rendering Algorithm；Heterogeneous multi CPU-GPU synergetic computing；Image synthesis algorithm；