On-the-fly Compression of Simulating Data Using Time-Dependent Basis

Exascale computation enables numerical solution of large-scale and high-fidelity problems. Memory and I/O restriction impede data analysis and visualization of many high-fidelity scientific computing applications — particularly transient problems. Therefore, developing an in situ compression method to compress the streaming data in real-time is vital. We present an on-the-fly dimension reduction technique that does not require calculation of large-scale eigenvalues problems. Instead, a scalable algorithm for the on-the-fly decomposition of the streaming data into a set of time-dependent bases and a core tensor is presented. The presented method is adaptive, and the reduction error is controlled by mode addition or removal. Several demonstration examples, including on-the-fly compression of the direct numerical simulation of turbulent flow, are presented.