Abstract
A wavelet-based method for compression of three-dimensional simulation data is presented and its software framework is described. It uses wavelet decomposition and subsequent range coding with quantization suitable for floating-point data. The effectiveness of this method is demonstrated by applying it to example numerical tests, ranging from idealized configurations to realistic global-scale simulations. The novelty of this study is in its focus on assessing the impact of compression on post-processing and restart of numerical simulations.Graphical abstract
Highlights
Partial differential equations often arise in physical sciences from three-dimensional (3D) continuum models, yielding boundary value problems for continuous field variables defined over 3D spatial domains
Regular grids prevail in the high-performance computing (HPC) for enabling fast and efficient implementation of high-order numerical methods with good parallel scalability
HPC simulations based on Cartesian grids are extremely diverse and include, to name a few, particle-laden fluid flows (Fornari et al 2016), solar flares (Bakke et al 2018) and neutron transport inside the core of a nuclear reactor (Baudron and Lautard 2007)
Summary
Partial differential equations often arise in physical sciences from three-dimensional (3D) continuum models, yielding boundary value problems for continuous field variables defined over 3D spatial domains. It is a common practice to store the simulation output fields in single precision and down-sample the data, e.g., save every second point value in each direction Such reduced datasets, while being of insufficient information capacity for the simulation, are often suitable for post-processing. It should be mentioned that wavelet bases are not the only that yield sparse representation of turbulent flow fields Decompositions such as proper orthogonal decomposition (POD, Berkooz et al 1993; Balajewicz et al 2013) or dynamic mode decomposition (DMD, Schmid 2010) are used for this purpose and employed in CFD output data compression methods (Lorente et al 2010; Bi et al 2014).
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