Learning to Compress Unstructured Mesh Data from Simulations

The amount of data output from a computer simulation has grown to terabytes and petabytes as increasingly-complex simulations are being run on massively-parallel systems. As we approach exa-flop computing in the next decade, it is expected that the I/O subsystem will not be able to write out these large volumes of data. In this paper, we explore the use of machine learning to compress the data before it is written out. Despite the computational constraints that limit us to using very simple learning algorithms, our results show that machine learning is a viable option for compressing unstructured data. Further, by using a better sampling algorithm to generate the training set, we can obtain more accurate results compared to random sampling, at no extra cost.