## 1 Introduction
User-defined importance metrics and filters are used to determine the importance of blocks; contiguous and convex regions of data that combined make up the generated simulation data. The calculated importance can then be used for a wide variety of purposes to accelerate or guide the simulation, such as identifying important regions, down-sample, reducing, compressing, or simply removing parts of the data based on user-defined constraints and the underlying hardware. What sets the proposed method apart from related work is its ability to calculate importance when using multiple analyses  
Figure 1: In-transit co-processing. The simulation is performed on one set of nodes, whereas visualization, analysis, and I/O operations are performed on a different set (transit nodes). efficiently. It can adaptively make any number of decisions based on the importance, compared to only a binary decision, which is typical in other methods. We also propose an approach that uses the proposed method to identify how to best combine the usage of multiple different compression methods based on the importance of the underlying data, which can be seen as a use case of the proposed method. In the case of lossy compression, loss of detail in regions of interest can be kept at a minimum by reducing unimportant areas. Using this data-driven approach, we strive to reduce the data size and the in-transit data transfer time to accelerate in-transit co-processing. The main contributions of this work are as follows:  
1. An efficient method to determine the block importance of large-scale simulations. Calculating the importance of various regions of data was a time-consuming task in many related works. As such, only a few importance metrics tend to be used. The main goal of our proposed method is to provide researchers with a customizable and easy-to-use way to efficiently determine the importance of subsets of large-scale simulation data, even when using multiple complex analyses to determine such importance. To reach this goal, we have developed a scheme that uses a separate buffer to transform the data to a more suitable structure for analysis and strives to schedule importance analyses in a fashion that improves the cache hit rates. We also introduce the usage of an adaptive condition window; a custom range within which multiple condition parameters can vary. Using a condition window allows parameters to change based on the current constraints of the environment. For example, if a data transfer is too time-consuming, the parameters can be adapted to compress or remove more data in the coming time step. 2. A flexible approach to accelerate in-transit co-processing. We observe that the effectiveness of a certain compression algorithm often depends on the underlying compressed data. By identifying the importance and the most suitable compression for each region of generated simulation data, we strive to minimize the data size and the in-transit data transfer time by combining the use of multiple compression methods integrated into a pipeline. The approach explored in this paper puts no additional restraints on the contents of the co-processing stage. It can be used in tandem with various visualization software, e.g., Paraview [18], VisIt [19], or OSPRay [20]. 3. A case study of how the in-transit co-processing of a Richtmyer-Meshkov instability (RMI) simulation can be accelerated using the proposed approach. The RMI simulation is performed using CNS3D, a state-of-the-art program for numerical fluid simulations. The structure of this paper is organized as follows. Related work is discussed in Section 2. The proposed approach is presented in Section 3. In Section 4, we evaluate the performance of the proposed approach. Lastly, our conclusions are presented in Section 5.