Clemson University Clemson, United States of America
Due to I/O bandwidth limitations, intelligent in situ data reduction methods are needed to enable post-hoc workflows. Current state-of-the-art sampling methods save data points if their region is deemed spatially or temporally important. By analyzing the properties of the data values at each time-step, two consecutive steps may be found to be very similar. This research follows the notion that if neighboring time-steps are very similar, samples from both are unnecessary, which leaves storage for more useful samples to be chosen. Here, we present an investigation of the combination of spatial and temporal sampling to drastically reduce data size without loss of valuable information. We demonstrate that by reusing samples, our reconstructed dataset is able to reduce the overall data size while achieving a higher post-reconstruction quality over other reduction methods.