Approximate Puzzlepiece Compositing

TL;DR

This paper introduces Approximate Puzzlepiece Compositing, a scalable distributed rendering algorithm that efficiently visualizes complex, non-convex mesh partitions common in large-scale simulations, with minimal communication overhead.

Contribution

It presents a novel order-independent compositing method that handles irregular mesh partitions without data re-partitioning, improving performance and image quality in large-scale CFD visualization.

Findings

Achieves high image quality comparable to state-of-the-art methods.

Demonstrates scalability with minimal overhead on HPC systems.

Effectively visualizes complex unstructured mesh data.

Abstract

The increasing demand for larger and higher fidelity simulations has made Adaptive Mesh Refinement (AMR) and unstructured mesh techniques essential to focus compute effort and memory cost on just the areas of interest in the simulation domain. The distribution of these meshes over the compute nodes is often determined by balancing compute, memory, and network costs, leading to distributions with jagged nonconvex boundaries that fit together much like puzzle pieces. It is expensive, and sometimes impossible, to re-partition the data posing a challenge for in situ and post hoc visualization as the data cannot be rendered using standard sort-last compositing techniques that require a convex and disjoint data partitioning. We present a new distributed volume rendering and compositing algorithm, Approximate Puzzlepiece Compositing, that enables fast and high-accuracy in-place rendering of AMR and unstructured meshes. Our approach builds on Moment-Based Ordered-Independent Transparency to achieve a scalable, order-independent compositing algorithm that requires little communication and does not impose requirements on the data partitioning. We evaluate the image quality and scalability of our approach on synthetic data and two large-scale unstructured meshes on HPC systems by comparing to state-of-the-art sort-last compositing techniques, highlighting our approach's minimal overhead at higher core counts. We demonstrate that Approximate Puzzlepiece Compositing provides a scalable, high-performance, and high-quality distributed rendering approach applicable to the complex data distributions encountered in large-scale CFD simulations.