# Real-Time Interactive Parallel Visualization of Large-Scale Flow-Field Data
## 4 Real-Time Interactive Methods for Large-Scale Flow Fields  
In response to the visualization needs of large-scale complex scenarios, the computationally intensive processes of visual feature extraction and parallel rendering must be performed on supercomputers due to the vast amount of data involved. Therefore, researchers have been devoted to exploring parallel rendering architectures, gradually overcoming challenges such as image parallel rendering, image composition, and image transmission. However, the parallel visualization of large-scale flow fields still faces a sig  
Figure 6: Grid construction and mapping. As indicated by the blue and orange regions in the figure, each process reconstructs its sub-grid and remaps the grid IDs. nificant technical challenge: how to drive the usage of supercomputers from visualization terminals, enabling complex visual analysis while satisfying users' interactive requirements and achieving real-time feedback for interactive rendering. From the perspective of supercomputer architecture, the visualization terminals are connected to the rendering nodes through Ethernet connections (with bandwidths ranging from hundreds of megabits to gigabits per second), while high-speed interconnects, such as Intel InfiniBand (with bandwidths of tens of gigabits per second), are employed between the rendering nodes. The difference in bandwidth between these two types of connections can range from several hundred to several thousand times. Consequently, the multi-level asymmetric communication problem between visualization terminals and rendering nodes has become one of the bottlenecks in large-scale scientific visualization for real-time interaction. However, researchers have not thoroughly investigated this problem. The complexity of supercomputer system architecture, coupled with the requirement for real-time human-computer interaction in scientific visualization, renders existing parallel rendering architectures unsuitable for current supercomputer system architectures. In this paper, we address this issue by managing different communication networks within the supercomputer system, leveraging communication resources effectively, and achieving efficient parallel rendering that is tailored to meet the practical needs of real-time interaction in scientific visualization. Figure 7 shows this network communication management method. The visualization terminal runs on a service node while multiple rendering processes are launched on the compute nodes. The first step is to establish a connection between the visualization terminal and the compute nodes using Socket communication. Next, we use a data-partitioning strategy based on grid segmentation to parallel load data into each compute node. During this process, we construct metadata for the geometric model and use metadata parsing to replace serial I/O byte-by-byte file reading, reducing the number of process communications between rendering nodes, as well as the communication overhead of the server-side high-speed interconnect system. After data loading is complete, the service node initializes the image-rendering window and sends information, such as window size, position, and background, to the rendering master node via the Socket interface. The master node then broadcasts the window initialization information to the other rendering nodes using the MPI communication interface, awakening satellite processes to update the renderer information. The service node then initiates a remote call via Socket to issue an image-rendering command to the rendering nodes. Each rendering node uses visual feature  
Figure 7: Multi-level asymmetric communication management. After loading data, the user interacts and sends control commands to the master process via Socket. The master process awakens the child processes to begin rendering. After undergoing primitive transformation, data compression, data exchange, and other processes, the image is composited in the master process using the binary-swap algorithm.