The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Workshop: ISAV23: In Situ Infrastructures for Enabling Extreme-Scale Analysis and Visualization
Authors: Estelle Dirand, Ali Asad, and Yann Magnin (TotalEnergies SE)
Abstract: Facing the need for carbon emission reduction, processes such as CO2 capture in nanoporous Metal-Organic Frameworks (MOFs) have emerged. However, such processes still need to be improved, by understanding the dynamic properties of CO2 molecules when confined in MOF nanopores. To do so, molecular dynamics (MD) simulations are run for several millions of iterations, enabling to accurately compute the CO2 residency time. Nevertheless, this dynamical parameter remains challenging to compute by standard post-processing approaches and may require terabytes of memory when data are saved after each iteration. To tackle this issue, we developed a trigger-based in situ approach that saves only the relevant data. We implement it by instrumenting the LAMMPS MD code with the SENSEI/Python in situ API. We show that this approach reduces the quantity of data saved by 4 orders of magnitude and can be up to 14% faster than traditional MD simulations without in situ processing.