Abstract
Fluid flow in porous media is at the heart of problems such as
groundwater contamination and carbon sequestration, and
presents an important challenge for scientific computing.
For this class of problem, large three-dimensional simulations
are performed to advance scientific inquiry. On massively
parallel computing systems, the volume of data generated
by such approaches can become a productivity bottleneck
if the raw data generated from the simulation is analyzed
in a post-processing step. We present a physics-based
framework for in situ data reduction that is theoretically
grounded in multiscale averaging theory. We show how task
parallelism can be exploited to concurrently perform a variety
of analysis tasks with data-dependent costs, including
the generation of iso-surfaces, morphological analyses,
and connected components analysis. A task management
framework is constructed to launch asynchronous analysis
threads, manage dependencies between different tasks, promote
data locality and hide the impact of data transfers.
The framework is applied to analyze GPU-based simulations
of two-fluid flow in porous media, generating a set of
averaged measures that represents the overall system behavior.
We demonstrate how the approach can be applied
to perform physically-consistent averaging over fluid subregions
using connected components analysis. Simulations
performed on Oak Ridge National Lab’s Titan supercomputer
are profiled to demonstrate the performance of the
associated multi-threaded in situ analysis for typical production
simulation of two-fluid flow.
