Scalable systems software---From mesh generation to scientific visualization

TLDR

Parallel supercomputing has traditionally focused on the solver kernel, leaving problem description and output interpretation as offline, sequential tasks, a division that becomes untenable as simulations scale beyond the terascale into the petascale. The authors propose an end‑to‑end approach that tightly couples meshing, partitioning, solver, and visualization, executing in parallel with shared data structures and no intermediate I/O. They implement this approach in an octree‑based finite element simulation of earthquake ground motion. Performance tests on up to 2048 processors demonstrate that the end‑to‑end method overcomes the scalability bottlenecks of the traditional pipeline.

Abstract

Parallel supercomputing has traditionally focused on the inner kernel of scientific simulations: the solver. The front and back ends of the simulation pipeline - problem description and interpretation of the output - have taken a back seat to the solver when it comes to attention paid to scalability and performance, and are often relegated to offline, sequential computation. As the largest simulations move beyond the realm of the terascale and into the petascale, this decomposition in tasks and platforms becomes increasingly untenable. We propose an end-to-end approach in which all simulation components - meshing, partitioning, solver, and visualization - are tightly coupled and execute in parallel with shared data structures and no intermediate I/O. We present our implementation of this new approach in the context of octree-based finite element simulation of earthquake ground motion. Performance evaluation on up to 2048 processors demonstrates the ability of the end-to-end approach to overcome the scalability bottlenecks of the traditional approach