Data Exploration at the Exascale

Childs, Hank

doi:10.14529/jsfi150301

Cited by 3 publications

(1 citation statement)

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“…Data sampling is a popular data reduction method that saves a subset of data values and uses this subset to reconstruct missing data when the whole dataset is needed. Some existing sampling methods utilize uniform random selection techniques to determine which points to keep (Woodring et al, 2011; Childs 2015; Wei et al, 2018), while others focus on preserving specific regions of interest (ROI) in the data (Biswas et al 2018, 2020a; Nouanesengsy et al, 2014). In our prior work, we develop a spatio-temporal hybrid data sampling method that biases rare data values and leverages a dataset’s temporal aspect to achieve higher post-reconstruction quality (Fulp et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Accelerated dynamic data reduction using spatial and temporal properties

Fulp

Zou

et al. 2023

The International Journal of High Performance Computing Applica

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Due to improvements in high-performance computing (HPC) capabilities, many of today’s applications produce petabytes worth of data, causing bottlenecks within the system. Importance-based sampling methods, including our spatio-temporal hybrid data sampling method, are capable of resolving these bottlenecks. While our hybrid method has been shown to outperform existing methods, its effectiveness relies heavily on user parameters, such as histogram bins, error threshold, or number of regions. Moreover, the throughput it demonstrates must be higher to avoid becoming a bottleneck itself. In this article, we resolve both of these issues. First, we assess the effects of several user input parameters and detail techniques to help determine optimal parameters. Next, we detail and implement accelerated versions of our method using OpenMP and CUDA. Upon analyzing our implementations, we find 9.8× to 31.5× throughput improvements. Next, we demonstrate how our method can accept different base sampling algorithms and the effects these different algorithms have. Finally, we compare our sampling methods to the lossy compressor cuSZ in terms of data preservation and data movement.

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Section: Introductionmentioning

confidence: 99%

Accelerated dynamic data reduction using spatial and temporal properties

Fulp

Zou

et al. 2023

The International Journal of High Performance Computing Applica

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Relationship-aware Multivariate Sampling Strategy for Scientific Simulation Data

Hazarika

Biswas

Wolfram

et al. 2020

2020 IEEE Visualization Conference (VIS)

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With the increasing computational power of current supercomputers, the size of data produced by scientific simulations is rapidly growing. To reduce the storage footprint and facilitate scalable post-hoc analyses of such scientific data sets, various data reduction/summarization methods have been proposed over the years. Different flavors of sampling algorithms exist to sample the high-resolution scientific data, while preserving important data properties required for subsequent analyses. However, most of these sampling algorithms are designed for univariate data and cater to post-hoc analyses of single variables. In this work, we propose a multivariate sampling strategy which preserves the original variable relationships and enables different multivariate analyses directly on the sampled data. Our proposed strategy utilizes principal component analysis to capture the variance of multivariate data and can be built on top of any existing state-of-the-art sampling algorithms for single variables. In addition, we also propose variants of different data partitioning schemes (regular and irregular) to efficiently model the local multivariate relationships. Using two real-world multivariate data sets, we demonstrate the efficacy of our proposed multivariate sampling strategy with respect to its data reduction capabilities as well as the ease of performing efficient post-hoc multivariate analyses.

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