# Real-Time Interactive Parallel Visualization of Large-Scale Flow-Field Data
## 5 Results
### Validation of Effectiveness  
For preprocessing methods of large-scale visual data, the primary objective is to ensure that the program can generate accurate visual images and guarantee the correctness of visualization computations. Additionally, the speedup ratio (the ratio of the serial execution time to the parallel execution time) is also a key performance metric for evaluation. To evaluate the data-partitioning strategy based on grid segmentation proposed in this paper, we conducted experiments by comparing it with traditional serial reading methods and the open-source scientific visualization software ParaView (V5.11.0). The visualization analysis was performed on a dataset with a scale of tens of millions of grids. In the experiment, we used 2, 4, 8, 16, 32, and 64 processes to read the datasets, respectively. Due to overheads such as process communication and context switching, the execution time of each process varied. Therefore, we took the time consumed by the last completed process as a valid experimental result. To avoid data randomness, each group of experiments was repeated five times and the average value was taken. Additionally, the speedup ratios of the grid-partitioning method and ParaView were calculated, respectively. Table 2 shows our experimental results. Since the EnSight Gold format is commonly used to store unsteady \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **Total (G)** & **Geo (G)** & **Variable (G)** & **Part** & **Point** & **Cell** \\ \hline DATSET1 (34) & 0.18 & 0.14 & 0.04 & 13 & 1,737,895 & 6,813,683 \\ DATSET2 (26) & 1.00 & 0.57 & 0.43 & 1 & 12,084,210 & 11,254,272 \\ DATSET3 (168) & 0.83 & 0.58 & 0.25 & 8 & 10,920,710 & 17,963,212 \\ DATSET4 (60) & 3.00 & 2.16 & 0.84 & 1 & 45,000,000 & 44,611,099 \\ DATSET5 (48) & 2.95 & 2.04 & 0.91 & 19 & 40,839,722 & 60,688,365 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Detailed information on the datasets. data, the datasets selected in this paper are also unsteady data. For ease of presentation, Table 2 only provides the experimental results of the first time step. The experimental results demonstrate that the proposed grid-based data-partitioning strategy effectively organizes the partitioning of flow-field data with tens of millions of grid cells. In each group of experiments conducted on three datasets, the speedup ratio of the proposed strategy significantly outperformed ParaView. We discovered that ParaView's data-partitioning strategy involves serially processing the tens of millions of grid cells in each process and then filtering and distributing them to the respective computational nodes. This approach not only increases program execution time but can even exceed serial reading. Such a data-partitioning strategy only improves system throughput and scalability. In contrast, the method presented in this paper utilizes geometric model metadata to allow each process to independently handle data for its own viewport. This significantly reduces the parallel reading time for datasets with tens of millions of grids to approximately one-seventh of the serial reading time. Figure 8 showcases the visualization images rendered using the proposed method on a dataset with tens of millions of grids, highlighting different characteristics such as cutting planes, streamlines, and surface textures. These images vividly demonstrate the correct segmentation and allocation of large-scale flow-field data, as well as the extraction and rendering of visual features achieved by the proposed method. To evaluate the impact of the grid-based segmentation method on the parallel reading of all time steps of the dataset, this study initiated 16 processes to load all time steps of three million-level grid datasets. The total consumption time was recorded and compared with serial loading. Figure 9 shows the experimental results.