Guoxi Liu

2024

1. 

   Efficient Representation and Analysis for A Large Tetrahedral Mesh using Apache Spark

   Yuehui Qian, Guoxi Liu, Federico Iuricich, and Leila De Floriani

   In IEEE Workshop on Topological Data Analysis and Visualization (TopoInVis), Best Paper Award, Oct 2024

   Abs Bib Code Supp

   Tetrahedral meshes are widely used due to their flexibility and adaptability in representing changes of complex geometries and topology. However, most existing data structures struggle to efficiently encode the irregular connectivity of tetrahedral meshes with billions of vertices. We address this problem by proposing a novel framework for efficient and scalable analysis of large tetrahedral meshes using Apache Spark. The proposed framework, called \textitTetra-Spark, features optimized approaches to locally compute many connectivity relations by first retrieving the \textitVertex-Tetrahedron (VT) relation. This strategy significantly improves Tetra-Spark’s efficiency in performing morphology computations on large tetrahedral meshes. To prove the effectiveness and scalability of such a framework, we conduct a comprehensive comparison against a vanilla Spark implementation for the analysis of tetrahedral meshes. Our experimental evaluation shows that Tetra-Spark achieves up to a 78\times speedup and reduces memory usage by up to 80% when retrieving connectivity relations with the VT relation available. This optimized design further accelerates subsequent morphology computations, resulting in up to a 47.7\times speedup.

   @inproceedings{Qian2024Efficient,
     author = {Qian, Yuehui and Liu, Guoxi and Iuricich, Federico and De Floriani, Leila},
     title = {Efficient Representation and Analysis for A Large Tetrahedral Mesh using Apache Spark},
     booktitle = {IEEE Workshop on Topological Data Analysis and Visualization (TopoInVis), Best Paper Award},
     month = oct,
     year = {2024},
     address = {Tampa, Florida},
     organization = {IEEE},
     dimensions = {true},
   }

2. 

   A Task-Parallel Approach for Localized Topological Data Structures

   Guoxi Liu, and Federico Iuricich

   IEEE Transactions on Visualization and Computer Graphics, Jan 2024

   Abs Bib Code PDF Supp Website

   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.

   @article{Liu2023Task,
     author = {Liu, Guoxi and Iuricich, Federico},
     journal = {IEEE Transactions on Visualization and Computer Graphics},
     title = {A Task-Parallel Approach for Localized Topological Data Structures},
     booktitle = {2023 IEEE Visualization Conference (VIS)},
     year = {2024},
     month = jan,
     volume = {30},
     number = {1},
     pages = {1271-1281},
     publisher = {IEEE},
     doi = {10.1109/TVCG.2023.3327182},
     issn = {1941-0506},
     dimensions = {true},
   }

2023

1. 

   TopoCluster: A Localized Data Structure for Topology-Based Visualization

   Guoxi Liu, Federico Iuricich, Riccardo Fellegara, and Leila De Floriani

   IEEE Transactions on Visualization and Computer Graphics, Feb 2023

   Abs Bib Code HTML PDF Website

   Unstructured data are collections of points with irregular topology, often represented through simplicial meshes, such as triangle and tetrahedral meshes. Whenever possible such representations are avoided in visualization since they are computationally demanding if compared with regular grids. In this work, we aim at simplifying the encoding and processing of simplicial meshes. The article proposes TopoCluster, a new localized data structure for tetrahedral meshes. TopoCluster provides efficient computation of the connectivity of the mesh elements with a low memory footprint. The key idea of TopoCluster is to subdivide the simplicial mesh into clusters. Then, the connectivity information is computed locally for each cluster and discarded when it is no longer needed. We define two instances of TopoCluster. The first instance prioritizes time efficiency and provides only a modest savings in memory, while the second instance drastically reduces memory consumption up to an order of magnitude with respect to comparable data structures. Thanks to the simple interface provided by TopoCluster, we have been able to integrate both data structures into the existing Topological Toolkit (TTK) framework. As a result, users can run any plugin of TTK using TopoCluster without changing a single line of code.

   @article{Liu2021Topocluster,
     title = {TopoCluster: A Localized Data Structure for Topology-Based Visualization},
     author = {Liu, Guoxi and Iuricich, Federico and Fellegara, Riccardo and De Floriani, Leila},
     journal = {IEEE Transactions on Visualization and Computer Graphics},
     year = {2023},
     month = feb,
     volume = {29},
     number = {2},
     pages = {1506-1517},
     doi = {10.1109/TVCG.2021.3121229},
     issn = {1941-0506},
     dimensions = {true},
   }