1 research outputs found
Scalable In Situ Lagrangian Flow Map Extraction: Demonstrating the Viability of a Communication-Free Model
We introduce and evaluate a new algorithm for the in situ extraction of
Lagrangian flow maps, which we call Boundary Termination Optimization (BTO).
Our approach is a communication-free model, requiring no message passing or
synchronization between processes, improving scalability, thereby reducing
overall execution time and alleviating the encumbrance placed on simulation
codes from in situ processing. We terminate particle integration at node
boundaries and store only a subset of the flow map that would have been
extracted by communicating particles across nodes, thus introducing an
accuracy-performance tradeoff. We run experiments with as many as 2048 GPUs and
with multiple simulation data sets. For the experiment configurations we
consider, our findings demonstrate that our communication-free technique saves
as much as 2x to 4x in execution time in situ, while staying nearly as accurate
quantitatively and qualitatively as previous work. Most significantly, this
study establishes the viability of approaching in situ Lagrangian flow map
extraction using communication-free models in the future