EAGER: Mapping Fragmentation and Topology Optimization Concepts to GPUs

EAGER：将碎片和拓扑优化概念映射到 GPU

• 批准号: 1321661
• 负责人: Glaucio Paulino
• 金额: $ 30万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-01 至 2016-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1321661&HistoricalAwards=false
• 关键词: EAGER; Mapping; Fragmentation; Topology; Optimization

The objective of this Early-concept Grant for Exploratory Research (EAGER) is to create an effective algorithmic mapping of extrinsic cohesive fragmentation and topology optimization concepts to GPUs. The extrinsic cohesive fracture framework will be used for detailed investigation of dynamic fracture instability of brittle and quasi-brittle materials to properly explain the limiting crack speed in these materials as well as increased fracture resistance with crack speed. This framework will also allow multiscale investigations of heterogeneous materials at the mesoscale, accounting for large deformation behavior of a soft matrix with hard particles, including details of the graded interphasial zones with interfacial cracking. The GPU framework for topology optimization will consider realistic ground structures that can impact material design, such as the design of extreme materials (e.g. auxetic), which are globally homogenized but may locally (microstructurally) display a functionally graded material architecture. The mapping and parallelization techniques will be performed using NVIDIA's CUDA (Computer Unified Device Architecture) framework, however, other emerging architectures such as the Intel's MIC (many-integrated-core) can also be used and/or explored. To be able to fully utilize GPU hardware is an art that relies on the effectiveness of the algorithmic mapping associating software and hardware at various levels. To this effect, a tailored topological data structure will be created to support mesh modification and adjacency searches on the GPU. To circumvent race conditions, proper algorithms (e.g. mesh coloring) will be investigated together their impact on parallelization performance and concurrency issues. The research will make use of the National Center for Supercomputing Applications (NCSA) through collaboration with Dr. Volodymyr Kindratenko (Research Scientist, NCSA). The broader outcomes of this interdisciplinary research derive from the fact that GPUs have been a disruptive technology with great potential for non-graphics applications, such as in computational mechanics. This investigation will contribute to the understanding of both explicit and implicit algorithms by adopting surrogate problems for each case, namely, fragmentation and topology optimization, respectively. The scale of the problems to be addressed has the potential to lead to computational discovery through new physical understanding and insight. Concepts developed from this research will be adapted into the curriculum at the University of Illinois at Urbana-Champaign (UIUC). Educational and research findings will be disseminated broadly through the internet. Moreover, outreach activities will be conducted to motivate high-school students to pursue careers in engineering research and education.

这项对探索性研究的早期概念赠款的目的（急切）是对外在的凝聚力片段化和拓扑优化概念的有效算法映射。外在的内聚伤框架将用于详细研究脆性和准脆性材料的动态断裂不稳定性，以正确解释这些材料中的限制裂纹速度，并随着裂纹速度提高了断裂抗性。该框架还将允许对中尺度上的异质材料进行多尺度研究，从而考虑了带有硬颗粒的软基质的较大变形行为，包括带有界面裂纹的分级间相间区域的细节。拓扑优化的GPU框架将考虑可能影响材料设计的现实地面结构，例如极端材料的设计（例如辅助工具），这些材料（例如辅助工具）在全球均匀化，但可能在本地（微观结构上）显示功能分级的材料体系结构。将使用NVIDIA的CUDA（计算机统一设备体系结构）框架进行映射和并行化技术，但是，也可以使用和/或探索其他新兴体系结构，例如Intel的MIC（多集成核）。能够充分利用GPU硬件是一门依赖算法映射关联软件和硬件在各个层面上的有效性。为此，将创建一个量身定制的拓扑数据结构，以支持GPU上的网格修改和邻接搜索。为了避免种族条件，将研究适当的算法（例如网格着色）对它们对并行性能和并发问题的影响。这项研究将通过与Volodymyr Kindratenko博士（NCSA研究科学家）合作利用国家超级计算应用中心（NCSA）。这项跨学科研究的更广泛的结果源于GPU是一种具有巨大潜力非绘画应用的破坏性技术，例如计算机制。这项调查将通过分别为每种情况（即分裂和拓扑优化）采用替代问题来有助于对显式和隐式算法的理解。要解决的问题的规模有可能通过新的物理理解和洞察力导致计算发现。 这项研究开发的概念将适应伊利诺伊大学Urbana-Champaign（UIUC）的课程。教育和研究发现将通过互联网广泛传播。此外，将进行外展活动，以激发高中生从事工程研究和教育的职业。