CDI-Type I: A science and engineering programming and inverse modeling environment for massively parallel heterogeneous computing systems

CDI-Type I：大规模并行异构计算系统的科学和工程编程和逆向建模环境

• 批准号: 0941666
• 负责人: Martin Saar
• 金额: $ 60.41万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-10-01 至 2014-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0941666&HistoricalAwards=false
• 关键词: CDI; Type; science; engineering; programming

The goal of this project is to provide scientists and engineers with a generalized, hardware-independent programming and runtime environment that takes advantage of the wide range of scalable, high-performance, highly heterogeneous hardware systems available, while masking the details of each from the programmer. This environment will further provide an integrated inverse modeling tool to perform guided parameter space and uncertainty analysis as well as model optimization. Such inverse modeling is critical to identify general patterns in order to develop new scientific theories that characterize complexity and thus capture the essence of complex natural and engineered systems. In addition to improving our understanding of complexity in natural and built systems (primary CDI theme), the proposed environment will aid in visualizing and extracting knowledge from data (secondary CDI theme) for which inverse methods (e.g., deconvolutions, regressions, parameter space/uncertainty analysis, model optimization) are indispensable. Reaching these goals is only possible, however, when combining the highest-performing heterogeneous hardware solutions that can execute many thousands of simulations with a high-level programming and inverse modeling environment that effectively hides the heterogeneous hardware complexities. Several general computing classes are identified, with example applications from science and engineering, that are likely to benefit from such heterogeneous hardware implementations. Such implementations would then open up new, transformational opportunities in scientific and engineering computing, increasing the likelihood of discovery of new theories regarding multi-scale interactions, emergent behavior, pattern formation, and self-organization in complex, feedback-prone systems.Several fundamental scientific and engineering computing categories with example applications from seismology, volcanology, hydrodynamics, and rock magnetics have been identified as targets of opportunity for this type of heterogeneous computing.

该项目的目的是为科学家和工程师提供广义，独立的编程和运行时环境，以利用可用的各种可扩展，高性能，高度异构性的硬件系统，同时掩盖了程序员的详细信息。该环境将进一步提供一个集成的反建模工具，以执行指导参数空间和不确定性分析以及模型优化。这种反向建模对于确定一般模式至关重要，以开发新的科学理论来表征复杂性，从而捕获复杂的自然和工程系统的本质。除了提高我们对自然系统和构建系统（主要CDI主题）的复杂性的理解外，所提出的环境还将有助于从数据（次要CDI主题）中可视化和提取知识（例如，反向方法（例如，反应，回归，参数空间/不确定性分析，模型优化））。但是，只有在结合表现最高的异质硬件解决方案时才能实现这些目标，这些解决方案可以通过高级编程和反向建模环境来执行数千个模拟，从而有效地隐藏了异质性硬件复杂性。确定了几种一般计算类别，其中包括科学和工程的示例应用程序，这些应用程序可能会从这种异质硬件实现中受益。然后，这种实现将在科学和工程计算方面开辟新的，变革的机会，从而增加有关多尺度相互作用的新理论的可能性异构计算。