arXiv subjects

David Knapp

Publications and source records attributed to David Knapp.

3 recordsLinked to original sources

arXiv2026

A Morton-Type Space-Filling Curve for Pyramid Subdivision and Hybrid Adaptive Mesh Refinement

The forest-of-refinement-trees approach allows for dynamic adaptive mesh refinement (AMR) at negligible cost. While originally developed for quadrilateral and hexahedral elements, previous work established the theory and algorithms for unstructured meshes of simplicial and prismatic elements. To harness the full potential of tree-based AMR for three-dimensional mixed-element meshes, this paper introduces the pyramid as a new functional element type; its primary purpose is to connect tetrahedral and hexahedral elements without hanging edges. We present a well-defined space-filling curve (SFC) for the pyramid and detail how the unique challenges on the element and forest level associated with the pyramidal refinement are resolved. We propose the necessary functional design and generalize the fundamental global parallel algorithms for refinement, coarsening, partitioning, and face ghost exchange to fully support this new element. Our demonstrations confirm the efficiency and scalability of this complete, hybrid-element dynamic AMR framework.

cs.DC↗

arXiv2021

Predicting Process Name from Network Data

The ability to identify applications based on the network data they generate could be a valuable tool for cyber defense. We report on a machine learning technique capable of using netflow-like features to predict the application that generated the traffic. In our experiments, we used ground-truth labels obtained from host-based sensors deployed in a large enterprise environment; we applied random forests and multilayer perceptrons to the tasks of browser vs. non-browser identification, browser fingerprinting, and process name prediction. For each of these tasks, we demonstrate how machine learning models can achieve high classification accuracy using only netflow-like features as the basis for classification.

cs.CR↗

arXiv2019

An Optimized, Parallel Computation of the Ghost Layer for Adaptive Hybrid Forest Meshes

We discuss parallel algorithms to gather topological information about off-process mesh neighbor elements. This information is commonly called the ghost layer, whose creation is a fundamental, necessary task in executing most parallel, element-based computer simulations. Approaches differ in that the ghost layer may either be inherently part of the mesh data structure that is maintained and modified, or kept separate and constructed/deleted as needed. In this work, we present an updated design following the latter approach, which we favor for its modularity of algorithms and data structures. We target arbitrary adaptive, non-conforming forest-of-(oc)trees meshes of mixed element shapes, such as cubes, prisms, and tetrahedra, and restrict ourselves to face-ghosts. Our algorithm has low complexity and redundancy since we reduce it to generic codimension-1 subalgorithms that can be flexibly combined. We cover several existing solutions as special cases and optimize further using recursive, amortized tree searches and traversals.

cs.DC↗