Trigger Detection for Adaptive Scientific Workflows Using Percentile Sampling

DOI10.1137/15M1027942zbMath1386.68211arXiv1506.08258OpenAlexW2964269849MaRDI QIDQ2830613

Jacqueline H. Chen, Ali Pınar, C. Seshadhri, Maher Salloum, Ankit Bhagatwala, Janine C. Bennett

Publication date: 28 October 2016

Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1506.08258

zbMATH Keywords

adaptive workflowsublinear algorithmschemical explosive mode analysis (CEMA)in situ data analysisjudicious I/Oquantile samplingS3D

Mathematics Subject Classification ID

Parallel numerical computation (65Y05) Combustion (80A25)

Related Items

Anomaly detection in scientific data using joint statistical moments, Trigger Detection for Adaptive Scientific Workflows Using Percentile Sampling

Uses Software

• ParaView

Cites Work

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