Using feature importance metrics to detect events of interest in scientific computing applications

Ling, Julia; Kegelmeyer, W. Philip; Aditya, Konduri; Kolla, Hemanth; Reed, Kevin A.; Shead, Timothy M.; Davis, Warren L.

doi:10.1109/ldav.2017.8231851

Cited by 18 publications

(9 citation statements)

References 14 publications

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“…[MLF*16], Ling et al . [LKA*17] and Salloum et al . [SBP*15] have used statistical methods, machine learning algorithms and a sampling‐based approach to trigger time slice savings, respectively.…”

Section: Mesh Reductionmentioning

confidence: 93%

“…[SBP*15] have used statistical methods, machine learning algorithms and a sampling‐based approach to trigger time slice savings, respectively. The specific event detection approach most likely needs to be tailored to each simulation, and the computational overhead that is imposed on the simulation needs to be carefully considered [SBP*15, LKA*17].…”

Section: Mesh Reductionmentioning

confidence: 99%

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Data Reduction Techniques for Simulation, Visualization and Data Analysis

Marsaglia

Garth

et al. 2018

Computer Graphics Forum

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Data reduction is increasingly being applied to scientific data for numerical simulations, scientific visualizations and data analyses. It is most often used to lower I/O and storage costs, and sometimes to lower in‐memory data size as well. With this paper, we consider five categories of data reduction techniques based on their information loss: (1) truly lossless, (2) near lossless, (3) lossy, (4) mesh reduction and (5) derived representations. We then survey available techniques in each of these categories, summarize their properties from a practical point of view and discuss relative merits within a category. We believe, in total, this work will enable simulation scientists and visualization/data analysis scientists to decide which data reduction techniques will be most helpful for their needs.

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Section: Mesh Reductionmentioning

confidence: 93%

Section: Mesh Reductionmentioning

confidence: 99%

Data Reduction Techniques for Simulation, Visualization and Data Analysis

Marsaglia

Garth

et al. 2018

Computer Graphics Forum

View full text Add to dashboard Cite

show abstract

“…此外, 原位可视化技术 [13][14][15][16] 、网格约减技术 [17][18][19] 和时间采样技术 [20][21][22] 压缩技术 [23][24] 被认为是最佳的折中方案. Tao 等 [25] 提出了一种误差可控的有损压缩技术, 支持千核并行压缩 TB 量级的数据集, 可将误差控制在给定的阈值范围内.…”

Section: 相关工作unclassified

Error-Controlled Data Reduction Approach for Large-Scale Structured Datasets

Ai¹,

Leng²,

Xia³

et al. 2021

J Comput-Aided Design Comput Graphics

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The massive datasets generated by scientific or engineering simulations have reached terabytes (TB) or even petabytes (PB). Data reduction has thus become one of the most important tools for saving I/O and storage costs. In order to achieve high-precision visualization and analysis, an error-controlled data reduction approach is proposed for reducing structured large-scale datasets. Firstly, taken the difference between the resulting data and the original one as a constraint, a multi-level structured adaptively-refined background grid is constructed, according to the spatial distribution characteristics of the underlying physical fields. Secondly, the original data is interpolated and mapped to the background grid, and as a result, the data with much less cells is obtained and the storage cost is reduced. Finally, the reduced data is exported to the parallel file system in real time. The proposed data reduction algorithm is implemented based on the parallel programming framework named JASMIN. In this way, the algorithm can be directly coupled with the numerical simulation programs developed with JASMIN. Test results demonstrate that the parallel algorithm can be extended to tens of thousands of CPU cores in parallel. The proposed algorithm has been successfully applied to the electromagnetic simulation of unmanned aerial vehicle irradiation. The cell number of a structured dataset with one hundred billions cells is

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“…where the core tensor, S, has the same dimensions as T . The factor matrices U (1) , U (2) and U (3) are orthogonal matrices that result from "unfolding" (matricizing) T along modes 1, 2 and 3, respectively, and performing SVD over the resulting matrix. The symbol × k denotes a "mode-k" tensor-matrix product (see [14] for details).…”

Section: Higher Order Singular Value Decomposition (Hosvd)mentioning

confidence: 99%