## 5 Conclusions  
In this paper, we have presented a method to efficiently determine the importance of regions of interest of simulation data, emphasizing on using multiple importance metrics. In addition, we have presented a use case where the method was used to accelerate the in-transit co-processing of an RMI simulation by lowering the data transfer time. The approach to accelerate in-transit co-processing uses the importance of regions of interest to determine how to best combine the usage of multiple different compression methods on different subsets of the simulation data. Simulation data is analyzed to determine the importance of all regions of the 3D data sets, which is then used as a basis to utilize multiple compression and reduction methods adaptively.  
We have evaluated the performance of the proposed method and approach by conducting tests on two different compute clusters, using multivariate data from an RMI simulation. Our proposed method was able to expeditiously calculate block importance, even when multiple data probes were used. The above was especially the case for probes using sampled data, which could calculate accurate importance values at a much faster rate. The proposed method was also able to adaptively identify regions of important data in a reliable manner. The excellent scalability when using multiple data probes and low computation times also make the method viable to be used in many other in-situ and in-transit scenarios, such as: guiding the simulation; saving important data to permanent storage; or in tandem with additional analysis or visualization software. As for the approach, we were able to achieve better performance in all aspects (the compressed data size and the compression, data transfer, and decompression times) than the compression methods used for comparison. Compared to an RLE compression, using the proposed approach in a lossless scenario resulted in a speedup of up to 1.29\(\times\) for the overall execution time, and 2\(\times\) when performing data decompression. We conclude that the proposed approach significantly can accelerate the in-transit co-processing process.  
In future work, we will investigate how the proposed method can be used for other use cases. Furthermore, we plan to extend the proposed approach to work better with in-situ co-processing workflows.  
Conceptualization, M.W.; methodology, M.W., D.D. and I.K.; software, M.W., D.D. and I.K.; validation, M.W., D.D. and I.K.; formal analysis, M.W.; investigation, M.W.; resources, M.W., F.I, M.O. and D.D.; data curation, M.W., F.I. and M.O.; writing--original draft preparation, M.W., D.D. and I.K.; writing--review and editing, M.W., F.I., D.D. and M.O.; visualization, M.W.; supervision, F.I. and D.D.; project administration, M.W.; funding acquisition, F.I. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This study was supported in part by the Japan Society for the Promotion of Science KAKENHI Grant Numbers 20K21794.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The work was partly achieved through the use of the Octopus cluster at the Cybermedia Center, Osaka University. We would like to thank JHPCN and the Cybermedia Center at Osaka University for the generous use of the Octopus cluster.  
**Conflicts of Interest:** The authors declare no conflict of interest.