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Present day computational fluid dynamics simulations generate extremely largeamounts of data, sometimes on the order of TB/s. Often, a significant fractionof this data is discarded because current storage systems are unable to keeppace. To address this, data compression algorithms can be applied to dataarrays containing flow quantities of interest to reduce the overall amount ofstorage. Compression methods either exactly reconstruct the original dataset(lossless compression) or provide an approximate representation of the originaldataset (lossy compression). The matrix column interpolative decomposition (ID)can be implemented as a type of lossy compression for data matrices thatfactors the original data matrix into a product of two smaller factor matrices.One of these matrices consists of a subset of the columns of the original datamatrix, while the other is a coefficient matrix which approximates the columnsof the original data matrix as linear combinations of the selected columns.Motivating this work is the observation that the structure of ID algorithmsmakes them a natural fit for the asynchronous nature of task-based parallelism;they are able to operate independently on sub-domains of the system of interestand, as a result, provide varied levels of compression. Using the task-basedLegion programming model, a single-pass ID algorithm (SPID) for CFDapplications is implemented. Performance studies, scalability, and the accuracyof the compression algorithms are presented for an analytical Taylor-Greenvortex problem, followed by a large-scale implementation of a compressibleTaylor-Green vortex using a high-order Navier-Stokes solver. In both cases,compression factors exceeding 100 are achieved with relative errors at or below10e-3. Moreover, strong and weak scaling results demonstrate that introducingSPID to solvers leads to negligible increases in runtime.

Task-parallel in-situ temporal compression of large-scale computational fluid dynamics data