SCoReViS: Scalable Collaborative and Remote Visualization Software

SCoReViS：可扩展的协作和远程可视化软件

• 批准号: 0751397
• 负责人: Kelly Gaither
• 金额: $ 88.07万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-03-01 至 2013-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0751397&HistoricalAwards=false
• 关键词: SCoReViS; Scalable; Collaborative; Remote; Visualization

SCoReViS: Scalable Collaborative and Remote Visualization SoftwarePI: Kelly Gaither, University of Texas at AustinThis project addresses what is fast becoming the most common, most severe bottleneck in high-end computational science: the inability of users to analyze their data in real time because of the limitations of their local systems and limited network bandwidth that cannot support transfer of tremendous data sets. The project will bring data analysis and visualization capability in line with HPC modeling and simulation capability to enable new kinds of interaction, increasing the likelihood of discovery. The Challenge: Scientific visualization is a fundamental data analysis technique for simulation-based research. Through 2-D and 3-D images, scientific visualization helps scientists explore, make sense of, and communicate data, whether it is modeling a hurricane, tracing the arterial blood flow of a heart, or exploring a super massive black hole. However, high performance computing (HPC) systems capabilities are racing far ahead of users' ability to effectively visualize the data they produce. As petaflop systems produce simulation output of unprecedented scale, it is becoming unfeasible to move these enormous data sets over networks to visualization systems and to use special-purpose visualization systems to interact with them. To fully achieve the scientific impact of these costly tera-and petascale systems, we need to improve the visualization capabilities for high end users. As with HPC, this requires scalable visualization tools that aggregate the capabilities of many compute nodes, while rendering the data close to the source to eliminate costly network transfers. This requires remote visualization interfaces to these large-scale visualization systems, enabling remote users to work interactively with their data.The Solution: The development of the Scalable Collaborative and Remote Visualization Software (SCoReViS) is a direct response to the need for next-generation visualization tools for large-scale HPC platforms and dedicated graphics clusters. SCoReViS will:o Leverage the computational power of tera- and petascale HPC and large GPU-based graphics clusters, and scale seamlessly from the largest available platforms down to smaller systems deployed at scientists' local institutions;o Maximize the investments in large-scale systems by providing remote and collaborative access to visualization tools, making them available and effective for any researcher with a reasonably high-bandwidth network connection (e.g. consumer broadband service); o Provide general purpose visualization capabilities by supporting any software based on the standard OpenGL API, thus enabling most popular visualization applications; ando Create a platform of tools for scientists who compute at the largest scale and to promote the development of tools to address issues associated with large, time-dependent data sets. The key components will be assembled, optimized, packaged and supported to provide high performance, scalable remote/collaborative visualization on petascale HPC and graphics clusters.Impact: The potential impact of this project is immeasurable. The resulting software will be made available via open source licensing, enabling use by anyone with access to the TeraGrid, including diverse scientific disciplines. The remote and collaborative visualization capabilities enabled by SCoReViS will allow access by a broader community of researchers, increasing the ability of high performance computing (HPC) to address the largest problems facing us.

ScoreVIS：可扩展的合作和远程可视化软件：凯利·盖瑟（Kelly Gaither），德克萨斯大学奥斯汀大学项目的凯利·盖瑟（Kelly Gaither）解决了高端计算科学中最常见，最严重的瓶颈的迅速发展：由于无法实时分析其数据的本地系统和限制网络频段的限制，因此无法实时分析其数据，因此无法支持巨大的数据集。该项目将根据HPC建模和仿真能力一致地带来数据分析和可视化功能，以实现新型的相互作用，从而增加了发现的可能性。挑战：科学可视化是基于模拟研究的基本数据分析技术。通过2-D和3-D图像，科学可视化有助于科学家探索，了解和传达数据，无论是在建模飓风，追踪心脏的动脉血流还是探索超大的黑洞。 但是，高性能计算（HPC）系统功能远远超出了用户有效地可视化其产生数据的能力。随着PETAFLOP系统产生前所未有的量表的模拟输出，将这些庞大的数据集通过网络移动到可视化系统并使用特殊用途的可视化系统与它们进行交互变得不可行。为了充分实现这些昂贵的TERA和PETASCALE系统的科学影响，我们需要提高最终用户的可视化功能。与HPC一样，这需要可扩展的可视化工具来汇总许多计算节点的功能，同时将数据渲染到靠近源以消除昂贵的网络传输。这需要远程可视化接口到这些大规模可视化系统，使远程用户能够与其数据进行交互式工作。解决方案：可扩展协作和远程可视化软件（ScoreVIS）的开发是对大型HPC平台的下一代可视化工具的需求的直接响应。 SCOREVIS将：o利用TERA和PETASCALE HPC的计算能力以及大型基于GPU的图形簇，并从最大的可用平台到位于科学家本地机构部署的较小的系统中无缝地扩展； o最大程度地提高与可视化工具的远程和协作工具，可在大型系统中提供可用的工具，并有效地连接（有效地），并有效地（可提供），并有效地连接（有效）（有效）（有效）（有效）（有效）（有效地）宽带服务）； o通过支持基于标准OpenGL API的任何软件，提供通用可视化功能，从而实现最流行的可视化应用程序；安多（Ando）为科学家创建了一个工具平台，这些科学家以最大的规模计算并促进工具开发以解决与大型，时间有关的数据集相关的问题。关键组件将得到组装，优化，包装和支持，以在Petascale HPC和Graphics簇上提供高性能，可扩展的远程/协作可视化。Impact：该项目的潜在影响是不可估量的。所得软件将通过开源许可提供，从而可以使用访问Teragrid的任何人，包括各种科学学科。 ScoreVis实现的远程和协作可视化功能将允许更广泛的研究人员访问，从而提高了高性能计算（HPC）解决我们面临的最大问题的能力。