# Real-Time Interactive Parallel Visualization of Large-Scale Flow-Field Data
## 2 Related Works  
Our research focuses on efficient data access and real-time interactive control in large-scale parallel visualization of flow fields. In this section, we review the relevant work in these areas. Over the past two decades, researchers have extensively studied out-of-core techniques and parallel methods to meet the demands of real-time interactive visualization of large-scale flow fields. Many of these methods aim to accelerate I/O transfers between memory and disk to achieve efficient access to large-scale data. In 1997, [PERSON] et al. proposed an interactive streamline construction method based on unstructured tetrahedral grids. The algorithm uses an octree to partition the original data and reconstructs it into subsets that are stored in disk files for fast data retrieval [15]. This out-of-core algorithm significantly reduces the overhead of reading data from disk and achieves good memory performance. However, its algorithm has certain limitations and only supports tetrahedral grid types, making it unable to adapt to more than a dozen other unstructured grid types. In 2011, [PERSON] et al. proposed the GLEAN framework, which considers the characteristics of applications, analysis, and systems to facilitate simulation time data analysis and I/O acceleration while providing flexible interfaces to meet data analysis needs at the fastest speed [16]. GLEAN leverages the data semantics of applications and fully utilizes different system topologies and characteristics to accelerate I/O. However, this framework is based on two I/O parallel frameworks: PnetCDF and HDF5, thus only supporting netCDF and HDF5 data formats. In 2015, [PERSON] et al. addressed the data I/O problem in the parallel computing of geographic grids by constructing a mapping model from logical structure to physical storage structure of grid data and proposing a parallel read/write framework for grid data [17]. This framework can accurately read data with higher performance but is only applicable to the parallel computing of geographic grids and cannot match the I/O processes in the field of scientific visualization. In 2020, [PERSON] et al. proposed a parallel reading method based on fragmenting MBR (Master Boot Record), where the same data block distributed and stored on different nodes is divided into two fragments. When reading data from different nodes, only half of the original data needs to be read, reducing the amount of data read and improving the read performance of the storage system [18]. However, this parallel reading method is only applicable to distributed storage files and cannot be used for the parallel reading of centralized storage files. In 2021, [PERSON] et al. proposed a parallel reading method for grid data files with billions of grids and hundreds of GBs in scale. The method stores ultra-large-scale grid data files generated by multiple objects using file grouping, where each group contains multiple files, each containing multiple data partitions. During reading, multiple file processes are used to read files and send them to the corresponding non-file processes to achieve a balanced data load distribution [19]. This scheme uses files as the basic unit for parallel reading. Although EnSight Gold format files also store multiple time-step files, each time step's data need to be read separately according to different timings and then output as results that can be processed by visualization tools. Therefore, multiple files cannot be directly read in parallel. With the increasing demands for precision and resolution in numerical simulations, the scale of flow-field visualization data has also grown significantly. As a result, an increasing amount of research is focusing on how to achieve real-time interactive control of large-scale flow-field data. [PERSON] et al. proposed a parallel rendering method based on the sort-last architecture using a cluster system with multiple rendering nodes and one fusion node.