EAGER: Mapping Fragmentation and Topology Optimization Concepts to GPUs

EAGER的目标是为GPU创建一个有效的算法映射，将外部内聚碎片和拓扑优化概念映射到GPU。外部凝聚断裂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的极限裂纹速度以及随裂纹速度增加的断裂阻力。该框架还将允许在介观尺度上对非均质材料进行多尺度调查，解释具有硬颗粒的软基质的大变形行为，包括具有界面开裂的梯度相间区的细节。用于拓扑优化的GPU框架将考虑可能影响材料设计的真实地面结构，例如极端材料（例如拉胀）的设计，其是全局均质的，但可能局部（微观结构）显示功能梯度材料架构。映射和并行化技术将使用NVIDIA的CUDA（计算机统一设备架构）框架执行，然而，也可以使用和/或探索其他新兴架构，如英特尔的MIC（多集成核）。能够充分利用GPU硬件是一门艺术，它依赖于将各个级别的软件和硬件关联起来的算法映射的有效性。为此，将创建定制的拓扑数据结构以支持GPU上的网格修改和邻接搜索。为了规避竞争条件，适当的算法（例如网格着色）将被研究它们对并行化性能和并发问题的影响。该研究将通过与Volodymyr Kindratenko博士（研究科学家，NCSA）的合作，利用国家超级计算应用中心（NCSA）。这一跨学科研究的更广泛成果来自于这样一个事实，即GPU一直是一种具有巨大潜力的非图形应用（如计算力学）的颠覆性技术。这项调查将有助于理解显式和隐式算法采用代理问题的每种情况下，即，碎片和拓扑优化，分别。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。 本研究开发的概念将被改编为伊利诺伊大学厄巴纳-香槟分校（UIUC）的课程。教育和研究成果将通过互联网广泛传播。此外，还将开展外联活动，鼓励高中生从事工程研究和教育事业。

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.