The Effect of Lossy Data Compression in Computational Fluid Dynamics Applications: Resilience and Data Postprocessing
The field of computational fluid dynamics (CFD) is data intensive, particularly for high-fidelity simulations. Direct and large-eddy simulations (DNS and LES), which are framed in this high-fidelity regime, require to capture a wide range of flow scales, a fact that leads to a high number of degrees of freedom. Besides the computational bottleneck, brought by the size of the problem, a slightly overlooked issue is the manipulation of the data. High amounts of disk space and also the slow speed of I/O (input/output) impose limitations on large-scale simulations. Typically the computational requirements for proper resolution of the flow structures are far higher than those of post-processing. To mitigate such shortcomings we employ a lossy data compression procedure, and track the reduction that occurs for various levels of truncation of the data set.
Financial support from the Stiftelsen för strategisk forskning (SSF) and the Swedish e-Science Research Centre (SeRC) via the SESSI project is acknowledged. The computations were performed on resources provided by the Swedish National Infrastructure for Computing (SNIC).
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