Enabling adaptive scientific workflows via trigger detection

TL;DR

This paper presents a new methodology for detecting sudden heat release in turbulent combustion simulations, enabling adaptive workflows that respond dynamically to simulation states with improved robustness.

Contribution

It introduces a novel trigger detection method using sublinear techniques, enhancing adaptive workflow capabilities in scientific simulations.

Findings

Effective prediction of heat release in two use cases

Improved robustness with combined metrics

Demonstrated efficiency of sublinear trigger detection

Abstract

Next generation architectures necessitate a shift away from traditional workflows in which the simulation state is saved at prescribed frequencies for post-processing analysis. While the need to shift to in~situ workflows has been acknowledged for some time, much of the current research is focused on static workflows, where the analysis that would have been done as a post-process is performed concurrently with the simulation at user-prescribed frequencies. Recently, research efforts are striving to enable adaptive workflows, in which the frequency, composition, and execution of computational and data manipulation steps dynamically depend on the state of the simulation. Adapting the workflow to the state of simulation in such a data-driven fashion puts extremely strict efficiency requirements on the analysis capabilities that are used to identify the transitions in the workflow. In this paper we build upon earlier work on trigger detection using sublinear techniques to drive adaptive workflows. Here we propose a methodology to detect the time when sudden heat release occurs in simulations of turbulent combustion. Our proposed method provides an alternative metric that can be used along with our former metric to increase the robustness of trigger detection. We show the effectiveness of our metric empirically for predicting heat release for two use cases.