  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, as a function of the
number of samples. Most importantly, the number of samples is independent of
the problem size, thus our proposed algorithm offers perfect scalability. Our
experiments on homogeneous charge compression ignition (HCCI) and reactivity
controlled compression ignition (RCCI) simulations show that the proposed
method can detect rapid increases in heat release, and its computational
overhead is negligible. Our results will be used for dynamic workflow decisions
about data storage and mesh resolution in future combustion simulations.
Proposed framework is generalizable and we detail how it could be applied to a
broad class of scientific simulation workflows.
