What are the performance improvents we can introduce in a mesh adaptation code in order to take advantage of the current and upcoming supercomputers ? What about the parallel performance at a single computing node? In this project we explore how to the apply the telescopic-approach to mesh adaptation. The main focus is to create a mesh that matches the needs of each application while at the same time taking advantage of the underlining hardware.

    Design of the telescopic approach of the CRTC lab
    Weak scalability improvement of applying a data decomposition method to PODM. (Figure kindly provided by D. Feng)
    Comparison of CDT3D performance adapting a delta wing mesh from 50k metric complexity to 500k metric complexity (strong scaling, base-case is the sequential time of each software.)
    Octree adapted to the density of a mesh. This kind of octrees can be used to extract subdomains for parallel mesh adaptation
    Few selected subdomains of the previous partitioned mesh
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  1. P. Thomadakis, C. Tsolakis, and N. Chrisochoides, “Multithreaded Runtime Framework for Parallel and Adaptive Applications,” Engineering with Computers, vol. 38, no. 5, pp. 4675–4695, Jul. 2022.

  2. C. Tsolakis, P. Thomadakis, and N. Chrisochoides, “Tasking framework for Adaptive Speculative Parallel Mesh Generation,” The Journal of Supercomputing, vol. 78, no. 5, pp. 1–32, Apr. 2022.

  3. K. M. Garner, P. Thomadakis, T. Kennedy, C. Tsolakis, and N. Chrisochoides, “On the End-User Productivity of a Pseudo-Constrained Parallel Data Refinement Method for the Advancing Front Local Reconnection Mesh Generation Software,” in AIAA Aviation Forum, American Institute of Aeronautics and Astronautics, 2019.

  4. F. Drakopoulos, C. Tsolakis, and N. Chrisochoides, “Fine-grained Speculative Topological Transformation Scheme for Local Reconnection Methods,” AIAA Journal, vol. 57, no. 9, pp. 4007–4018, 2019.

  5. F. Drakopoulos, C. Tsolakis, and N. Chrisochoides, “Fine-grained Speculative Topological Transformation Scheme for Local Reconnection Method,” in AIAA Aviation Forum, Atlanta, Georgia, 2018.

  6. P. Thomadakis, C. Tsolakis, K. Vogiatzis, A. Kot, and N. Chrisochoides, “Parallel Software Framework for Large-Scale Parallel Mesh Generation and Adaptation for CFD Solvers,” in AIAA Aviation Forum, Atlanta, Georgia, 2018.

  7. N. Chrisochoides, A. Chernikov, T. Kennedy, C. Tsolakis, and K. Garner, “Parallel data refinement layer of a telescopic approach for extreme-scale parallel mesh generation for CFD applications,” in AIAA Aviation Forum, Atlanta, Georgia, 2018.

  8. K. M. Garner, T. Kennedy, C. Tsolakis, and N. Chrisochoides, “Stability of Advancing Front Local Reconnection for Parallel Data Refinement,” Norfolk,VA, 2018.

  9. D. Feng, C. Tsolakis, A. N. Chernikov, and N. P. Chrisochoides, “Scalable 3D Hybrid Parallel Delaunay Image-to-mesh Conversion Algorithm for Distributed Shared Memory Architectures,” Comput. Aided Des., vol. 85, no. C, pp. 10–19, Apr. 2017.

  10. D. Feng, C. Tsolakis, A. N. Chernikov, and N. P. Chrisochoides, “Scalable 3D Hybrid Parallel Delaunay Image-to-Mesh Conversion Algorithm for Distributed Shared Memory Architectures,” 24th International Meshing Roundtable, vol. 124, pp. 18–30, 2015.