CRII: CSR: GPU-Accelerated, Multi-Display Applications for Large Computer-Aide Design (CAD) Model Visualization on a Commodity Desktop

CRII：CSR：GPU 加速的多显示应用程序，用于商品桌面上的大型计算机辅助设计 (CAD) 模型可视化

• 批准号: 2000488
• 负责人: Chao Peng
• 金额: $ 0.8万
• 依托单位: Rochester Institute of Tech
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-13 至 2020-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2000488&HistoricalAwards=false
• 关键词: CRII; CSR; GPU; Accelerated; Multi

This project focuses on utilizing parallel architectures within graphical processing units (GPUs) to fill the need for real-time interaction with large datasets in Computer-Aided Design (CAD) systems. GPU-accelerated multi-display applications for visualizing large 3D datasets are important because new scientific discoveries and dynamic investigations of complex digital contents rely on visualization results, favoring with latencies measured in milliseconds rather than seconds and the rich information displayed with high-resolution on multiple screens. The potential of this work is an end-to-end solution for visualizing large 3D datasets on a commodity desktop and providing high-speed interactions and visual explorations of the datasets while having the advantage on cost savings.Even more specifically, the algorithms and visualization systems developed through this research will allow engineers and designers to interactively create, change, and visually analyze large datasets. By providing a parallel computational foundation on visualization further innovation in CAD research is possible. Graduate students involved will be trained in the principles and uses of advanced computer by designing and implementing GPU algorithms. In addition, creating applications that make good use of GPUs also impacts interactive learning, especially on graphics-assisted and multi-dimensional educational applications, by providing breakthrough technology to increase the performance of educational applications while displaying complex graphical contents.Traditionally, acceleration algorithms for rendering (e.g., view-dependent mesh simplification) are implemented sequentially in visualization applications, which are not suitable for fast processing the model, composed of large amount of geometric primitives. The algorithms developed in this project will support primitive-level parallelization on GPUs and enable dynamic adjustments of detail levels of 3D meshes upon the changes of camera configurations. Though multi-GPU applications are available commercially, load-balancing issues among GPUs have not been successfully addressed. Imbalanced workload distributions hurt overall performance and affect the quality of mesh renderings. This research will develop a screen-partitioning balancer to balance GPU workloads and employ a pipelined approach to conduct GPU-to-GPU and CPU-to-GPU streaming for efficient data management. The efficiencies of inter-device communications will be given an in-depth evaluation.

该项目的重点是利用图形处理单元（GPU）内的并行架构，以满足计算机辅助设计（CAD）系统中与大型数据集进行实时交互的需求。用于可视化大型3D数据集的GPU加速多显示器应用程序非常重要，因为新的科学发现和复杂数字内容的动态调查依赖于可视化结果，有利于以毫秒而不是秒为单位测量的延迟，以及在多个屏幕上以高分辨率显示的丰富信息。这项工作的潜力是一个端到端的解决方案，用于在商用桌面上可视化大型3D数据集，并提供高速交互和数据集的可视化探索，同时具有节省成本的优势。更具体地说，通过这项研究开发的算法和可视化系统将允许工程师和设计师交互式地创建，更改和可视化分析大型数据集。通过提供可视化的并行计算基础，CAD研究的进一步创新是可能的。参与的研究生将通过设计和实现GPU算法来培训高级计算机的原理和使用。此外，创建充分利用GPU的应用程序还影响交互式学习，特别是在图形辅助和多维教育应用程序上，通过提供突破性技术来提高教育应用程序的性能，同时显示复杂的图形内容。视图相关的网格简化）在可视化应用中顺序实现，这不适合于快速处理由大量几何图元组成的模型。该项目开发的算法将支持GPU上的连续级并行化，并能够在相机配置变化时动态调整3D网格的细节级别。虽然多GPU应用程序在商业上是可用的，但GPU之间的负载平衡问题尚未成功解决。不平衡的工作负载分布会损害整体性能并影响网格渲染的质量。这项研究将开发一个屏幕分区平衡器来平衡GPU的工作负载，并采用流水线方法来进行GPU到GPU和CPU到GPU的流传输，以实现高效的数据管理。将对设备间通信的效率进行深入评估。