## 2 Related Work
A quantitative comparison is difficult, as each work has a different feature set and use case. We, therefore, chose to evaluate our method independently, as was also done in related work [10; 11; 12]. Many visualization and analysis techniques have been proposed for in-situ and in-transit co-processing. Some aim to batch-render images of each time step of the simulation from multiple viewpoints, thus allowing researchers to interactively explore different regions of the simulation post-hoc [22; 23]. Other techniques focus on extracting features, metadata, or samples of the simulation [24; 25; 26; 2]. Using many different types of visualizations and analyses would generally be desired to extract as much information from the \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Method**} \\ \cline{2-7}  & **This Work** & **[10]** & **[11]** & **[12]** & **[13; 14; 15]** & **[16; 17]** \\ \hline Processing approach & In-transit & In-situ & Post-hoc & In-situ & In-situ & In-situ \\ Supports custom importance metrics & ✓ & ✓ & & ✓ & ✓ & \\ Decisions based on multiple metrics & ✓ & & ✓ & & & \\ Tracks time-varying data & & & ✓ & ✓ & & \\ Used to compress unimportant data & ✓ & ✓ & & & & ✓ \\  & lossless/lossy & lossy & & & & lossy \\ Multiple compression methods & ✓ & & — & — & — & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparing features of related work [10; 11; 12; 13; 14; 15; 16; 17] to this work. simulation as possible. However, each additional task would increase the execution time. The logical approach would be to use in-transit co-processing in such a scenario due to the often limited time available to perform such computations in-situ. To make in-transit co-processing a viable option, we need new techniques to achieve faster data transfers between simulation and transit nodes. Many researchers have developed methods to lower the data transfer time required to perform in-transit co-processing [5; 27; 28; 29]. Most works have focused on uniformly reducing or compressing data [5; 27; 28]. Although efficient, regions of interest within the data are reduced to the same extent as unimportant regions. Lossy compression methods such as zfp [30] and an extension of SZ [31] strive to lower the compression error by analyzing the entropy of the data. Data sets are split into blocks of size \(4\times 4\times 4\) and \(6\times 6\times 6\), respectively. A function is then used to predict a suitable level of compression for each block, based on the estimated compression error. These lossy methods have proved to achieve good performance [30; 31]. However, the fact that they are lossy makes them unusable in many use cases. We also note that these methods are unable to determine the importance of regions of data, and that although the level of compression varies, they ultimately use the same lossy compression method on the whole data set. Both of these compression methods can be integrated into the proposed approach, as presented in Section 3, to be used on a subset of the available data blocks. In summary, effectively performing in-transit co-processing of data generated by scientific simulations remains an important research topic in the field of high-performance computing. Many methods which uniformly reduce or compress data have been proposed. However, the characteristics of the simulation data can vary substantially depending on the current time step, meaning that the need for compression and analysis also can vary. Some techniques which analyze the characteristics of the data also exist. However, one of the most commonly used techniques, AMR, is often unable to efficiently calculate individual block importance because of the different data access pattern. Although alternatives [10; 11; 12; 16; 17] have been proposed, they are generally limited in scope or in their ability to perform multiple analyses to determine block importance or characteristics of different parts in the studied simulation data.