A Task-Parallel Approach for Localized Topological Data Structures

TL;DR

This paper introduces ACTOPO, a task-parallel data structure for unstructured meshes that proactively computes connectivity, achieving significant speedups and memory efficiency improvements over existing methods.

Contribution

The paper presents a novel task-parallel approach for mesh connectivity that allows different threads to perform different functions, improving performance and memory usage.

Findings

Up to 5x speedup over state-of-the-art methods

Similar memory footprint as the most memory-efficient structure

Comparable time performance to the fastest existing structure

Abstract

Unstructured meshes are characterized by data points irregularly distributed in the Euclidian space. Due to the irregular nature of these data, computing connectivity information between the mesh elements requires much more time and memory than on uniformly distributed data. To lower storage costs, dynamic data structures have been proposed. These data structures compute connectivity information on the fly and discard them when no longer needed. However, on-the-fly computation slows down algorithms and results in a negative impact on the time performance. To address this issue, we propose a new task-parallel approach to proactively compute mesh connectivity. Unlike previous approaches implementing data-parallel models, where all threads run the same type of instructions, our task-parallel approach allows threads to run different functions. Specifically, some threads run the algorithm of choice while other threads compute connectivity information before they are actually needed. The approach was implemented in the new Accelerated Clustered TOPOlogical (ACTOPO) data structure, which can support any processing algorithm requiring mesh connectivity information. Our experiments show that ACTOPO combines the benefits of state-of-the-art memory-efficient (TTK CompactTriangulation) and time-efficient (TTK ExplicitTriangulation) topological data structures. It occupies a similar amount of memory as TTK CompactTriangulation while providing up to 5x speedup. Moreover, it achieves comparable time performance as TTK ExplicitTriangulation while using only half of the memory space.