Performance-Constrained In Situ Visualization of Atmospheric Simulations

While many parallel visualization tools now provide in situ visualization capabilities, the trend has been to feed such tools with what previously was large amounts of unprocessed output data and let them render everything at the highest possible resolution. This leads to an increased run time of simulations that still have to complete within a fixed-length job allocation. In this paper, we tackle the challenge of enabling in situ visualization under performance constraints. Our approach shuffles data across processes according to its content and filters out part of it in order to feed a visualization pipeline with only a reorganized subset of the data produced by the simulation. Our framework monitors its own performance and reconfigures itself dynamically to achieve the best possible visual fidelity within predefined performance constraints. Experiments on the Blue Waters supercomputer with the CM1 simulation show that our approach enables a 5× speedup and is able to meet performance constraints