# Real-Time Interactive Parallel Visualization of Large-Scale Flow-Field Data
## 1 Introduction  
Computational fluid dynamics (CFD) numerical simulations often involve applications such as turbulence, vortex motion and separation characteristics, boundary layer transition, shock/boundary layer interactions, unsteady flows, and control, exhibiting multi-region, multi-medium, and multi-scale characteristics [1; 2; 3; 4; 5]. The high-dimensional, time-varying, and diverse nature of flow-field data (including unstructured grids, structured grids, and hybrid grids) requires handling complex data types in flow visualization. Additionally, the complexity of unsteady characteristics and physical models, as well as the high resolution of the flow-field grids (with grid numbers ranging from hundreds of millions to billions), leads to massive visualization data size (ranging from GB to PB) [6]. Consequently, large-scale flow-field data visualization poses significant challenges in terms of memory capacity, data I/O, graphic computation, geometric rendering, and network communication management. Traditional serial post-processing software can no longer meet the requirements for the real-time interactive control of large-scale flow-field data, necessitating the adoption of parallel visualization techniques. The process of parallel visualization for large-scale flow-field data involves data preprocessing, data organization and partitioning, parallel feature calculation, parallel rendering, and image synthesis stages [7]. In the data organization and partitioning stage, effective management of large-scale flow-field data to achieve efficient access is a key and challenging aspect of parallel visualization. Furthermore, for the entire parallel visualization process, driving the workflow from a workstation to utilize supercomputers for interactive visualization control while ensuring real-time processing of large-scale complex flow fields presents a major challenge for the parallel visualization of large-scale flow fields. During the preprocessing and computation stages of computational fluid dynamics (CFD), different computational domains may utilize various software tools for modeling and numerical solving, resulting in the generation of diverse data formats in the CFD-solving process [8]. These formats include EnSight Gold, CGNS, VTK, HDF5, PLOT3D, and Tecplot, among others. The EnSight Gold format has demonstrated advantages in visualization and scalability, and it has found widespread applications in industrial and scientific domains [9]. However, the majority of current research still employs a serial reading approach to handle EnSight Gold data files. This serial reading method is highly inefficient, leading to difficulties in real-time interaction and frequently causing program crashes due to exceeding memory capacity, rendering the file unreadable. Large-scale parallel visualization data preprocessing requires support for these rich data formats. However, the varying organizational structures and approaches among different file types present significant challenges in establishing a unified processing methodology. As far as we know, the open-source visualization software ParaView has proposed a data-partitioning strategy specifically targeting the EnSight Gold format [10]. However, experimental evidence suggests that this strategy merely partitions the data across processes or nodes, resulting in limited improvements in data access performance [11]. For researchers performing flow-field visualization analysis using the EnSight Gold format, an efficient data-partitioning strategy can help overcome the memory limitations of serial I/O and significantly enhance the speed of reading large-scale flow-field data [12]. Such a data-partitioning strategy is of great significance to the engineering practice of large-scale flow visualization.