Analyzing the Effect and Performance of Lossy Compression on Aeroacoustic Simulation of Gas Injector

Abstract

Computational fluid dynamic simulations involve large state data, leading to performance degradation due to data transfer times, while requiring large disk space. To alleviate the situation, an adaptive lossy compression algorithm has been developed, which is based on regions of interest. This algorithm uses prediction-based compression and exploits the temporal coherence between subsequent simulation frames. The difference between the actual value and the predicted value is adaptively quantized and encoded. The adaptation is in line with user requirements, that consist of the acceptable inaccuracy, the regions of interest and the required compression throughput. The data compression algorithm was evaluated with simulation data obtained by the discontinuous Galerkin spectral element method. We analyzed the performance, compression ratio and inaccuracy introduced by the lossy compression algorithm. The post processing analysis shows high compression ratios, with reasonable quantization errors.

Solution of the Radiative Transfer Equation for Vertically Inhomogeneous Media by Numerical Integration Solvers: Comparative Analysis

“…In simple words, dimensionality reduction means representing the initial data set with less number of parameters than it is initially represented. It can be considered as one of the lossy data compression paradigms [28]. Dimensionality reduction is crucial for stable and high-performance processing of spectral measurements.…”

WaveRange: wavelet-based data compression for three-dimensional numerical simulations on regular grids

“…Sub-band coding (SBC) (Røsten et al 2004) and the use of more general filter banks than the discrete wavelet (Duval and Røsten 2000) have been considered in the context of seismic data compression. Another, conceptually different family of methods can be described as prediction-based compression algorithms (see Najmabadi et al 2017;Lakshminarasimhan et al 2011;Liang et al 2018; and references therein) that exploit spatiotemporal patterns in the data. A comparative discussion of different existing approaches to scientific data reduction, including lossy data compression, can be found in a recent review paper by , also see surveys by Balsa Rodrı ´guez et al (2014); Beyer et al (2014) for GPU-based volume rendering applications.…”