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Trigger Detection for Adaptive Scientific Workflows Using Percentile Sampling by Bennett, Janine C.; Bhagatwala, Ankit; Chen, Jacqueline H.; Seshadhri, C.; Pinar, Ali; Salloum, Maher is a scholarly article available to read on EtoBox.

What is Trigger Detection for Adaptive Scientific Workflows Using Percentile Sampling about?

Increasing complexity of scientific simulations and HPC architectures are driving the need for adaptive workflows, where the composition and execution of computational and data manipulation steps dynamically depend on the evolutionary state of the simulation itself. Consider for example, the frequency of data storage. Critical phases of the simulation should be captured with high frequency and with high fidelity for post-analysis, however we cannot afford to retain the same frequency for the full simulation due to the high cost of data movement. We can instead look for triggers, indicators that the simulation will be entering a critical phase and adapt the workflow accordingly. We present a method for detecting triggers and demonstrate its use in direct numerical simulations of turbulent combustion using S3D. We show that chemical explosive mode analysis (CEMA) can be used to devise a noise-tolerant indicator for rapid increase in heat release. However, exhaustive computation of CEMA values dominates the total simulation, thus is prohibitively expensive. To overcome this bottleneck, we propose a quantile-sampling approach. Our algorithm comes with provable error/confidence bounds,

Author

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

Published

2015

Language

EN