Foresight: Analysis That Matters for Data Reduction
Foresight: Analysis That Matters for Data Reduction
复制标题
前瞻：对于数据缩减至关重要的分析
DOI：
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复制
发表时间：
2020
期刊：
影响因子：
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通讯作者：
J. Ahrens
中科院分区：
文献类型：
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作者：
Pascal Grosset;C. Biwer;Jesus Pulido;A. Mohan;Ayan Biswas;J. Patchett;Terece L. Turton;D. Rogers;D. Livescu;J. Ahrens
随着超级计算机计算能力的增强，模拟规模也在增加，这反过来又会产生更多数量级的数据。由于生成的数据通常会超过模拟的磁盘配额，因此许多模拟将受益于减少存储需求的数据减少技术。这些技术包括自动编码器、数据压缩算法和采样。有损压缩技术可以显著减少数据大小，但这种技术的代价是丢失信息，这可能会导致不正确的事后分析结果。为了帮助科学家确定他们可以获得的最佳压缩，同时保持分析的准确性，我们开发了Foresight，这是一个分析框架，使用户能够评估不同的数据缩减技术将如何影响他们的分析。我们使用宇宙学模拟的粒子数据、直接数值模拟的湍流数据和xRage的小行星撞击数据来演示Foresight如何帮助科学家为他们的模拟确定最佳的数据简化技术。
As the computation power of supercomputers increases, so does simulation size, which in turn produces orders-of-magnitude more data. Because generated data often exceed the simulation’s disk quota, many simulations would stand to benefit from data-reduction techniques to reduce storage requirements. Such techniques include autoencoders, data compression algorithms, and sampling. Lossy compression techniques can significantly reduce data size, but such techniques come at the expense of losing information that could result in incorrect post hoc analysis results. To help scientists determine the best compression they can get while keeping their analyses accurate, we have developed Foresight, an analysis framework that enables users to evaluate how different data-reduction techniques will impact their analyses. We use particle data from a cosmology simulation, turbulence data from Direct Numerical Simulation, and asteroid impact data from xRage to demonstrate how Foresight can help scientists determine the best data-reduction technique for their simulations.
DOI：
10.1109/ipdps.2018.00044
发表时间：
2018-05
期刊：
2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
影响因子：
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作者：
Tao Lu;Qing Liu;Xubin He;Huizhang Luo;E. Suchyta;J. Choi;N. Podhorszki;S. Klasky;M. Wolf;Tong Liu;Zhenbo Qiao
通讯作者：
Tao Lu;Qing Liu;Xubin He;Huizhang Luo;E. Suchyta;J. Choi;N. Podhorszki;S. Klasky;M. Wolf;Tong Liu;Zhenbo Qiao
了解用于超大规模宇宙学模拟的基于 GPU 的有损压缩
DOI：
10.1109/ipdps47924.2020.00021
发表时间：
2020
期刊：
The 34th IEEE International Parallel and Distributed Processing Symposium (IPDPS 2020
影响因子：
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作者：
Jin, Sian;Grosset, Pascal;Biwer, Christopher;Pulido, Jesus;Tian, Jiannan;Tao, Dingwen;Ahrens, James
通讯作者：
Ahrens, James