Internal:Annual Reports

Summary

Year 1:

The first year of this project aimed at investigating a tangible implementation plan for design a multi-layered algorithmic and software framework for 3D tetrahedral anisotropic parallel mesh generation and adaptation relying on error-based metrics.

The main accomplishments have been: 1. Identified (and in two cases started) the implementation of three mesh generation approaches for the lower chip-level (optimistic) implementation of 3D tetrahedral anisotropic parallel mesh generation and adaptation: (i) the default method/software based on sequential AFLR3 from MSU –identified basic operations and challenges, (ii) a new implementation (CDT3D) using known algorithms for anisotropic mesh generation and adaptation, but taking into account lessons learned from and new requirements for ODU’s telescopic approach and (iii) a completely new method in which an anisotropic 3D metric is embedded and isotropically meshed on a manifold in 4D dimensional space which could leverage ODU’s 4D Delaunay-based parallel optimistic I2M conversion technology. 2. Started the process of designing and implementing two out of four layers of the telescopic approach using TetGen (Delaunay-based open source mesh generation software): (i) Parallel Constrained (PC) layer that targets multiple racks of nodes, (ii) Parallel Data Refinement (PDR) layer that targets multiple nodes (within a single rack). 3. Identified test cases with NASA/LaRC (CAD model of a half DLR-F6 Airbus-type aircraft and a series of complex components from AIM@SHAPE repository) to evaluate mesh generation and adaptation modules. 4. Identified two state-of-the-art software runtime systems from industry, academia and national labs that meet the requirements of the proposed Multi-Layered Runtime System (MRTS): (i) OCR is a runtime system from Intel and (ii) Argo is an exascale-era ecosystem build by a collaboration effort between DoE labs (mainly ANL) and academia.

The results so far are promising. For example, the initial PDR implementation using TetGen and homegrown I2M conversion methods is stable (i.e., generate the same quality mesh with the sequential code, see Figure 1), robust (i.e., can mesh all the geometries tried –those the sequential code manages to process), with more than 95% code re-use and it is efficient both at: (i) the node-level (with 32 cores): the fixed speedup is more than 16 for a mesh with 800M element and scalable (weak) speedup is close to 20. (ii) the rack(s)-level (30 nodes with 20 cores each) using MPI+threads and CRTC’s I2M conversion software, the PDR.PODM’s scalable speedup is increasing linearly with a mesh 1.14 billion elements (using 600 cores). Figure 2 depicts the performance for both Distributed Shared Memory paradigm on cc-NUMA machines and Distributed Memory paradigm on a cluster of nodes at CRTC. We anticipate better performance for AFLR3 and PDR-like approach we plan to start implementing in Years 2 and 3 of this project.