We compare, at similar scales, the processes of microjetting and ejecta production from shocked roughened metal surfaces by using atomistic and continuous approaches. The atomistic approach is based on very large scale molecular dynamics (MD) simulations with systems containing up to 700 × 106 atoms. The continuous approach is based on Eulerian hydrodynamics simulations with adaptive mesh refinement; the simulations take into account the effects of viscosity and surface tension, and the equation of state is calculated from the MD simulations. The microjetting is generated by shock-loading above its fusion point a three-dimensional tin crystal with an initial sinusoidal free surface perturbation, the crystal being set in contact with a vacuum. Several samples with homothetic wavelengths and amplitudes of defect are simulated in order to investigate the influence of viscosity and surface tension of the metal. The simulations show that the hydrodynamic code reproduces with very good agreement the profiles, calculated from the MD simulations, of the ejected mass and velocity along the jet. Both codes also exhibit a similar fragmentation phenomenology of the metallic liquid sheets ejected, although the fragmentation seed is different. We show in particular, that it depends on the mesh size in the continuous approach.

The in situ paradigm proposes to co-locate simulation and analytics on the same compute node to analyze data while still resident in the compute node memory, hence reducing the need for postprocessing methods. A standard approach that proved efficient for sharing resources on each node consists in running the analytics processes on a set of dedicated cores, called helper cores, to isolate them from the simulation processes. Simulation and analytics thus run concurrently with limited interference. In this paper we show that the performance can be improved through a dynamic helper core strategy. We rely on a work stealing scheduler to implement TINS, a task-based in situ framework with an on-demand analytics isolation. The helper cores are dedicated to analytics only when analytics tasks are available. Otherwise the helper cores join the other cores for processing simulation tasks. TINS relies on the Intel R TBB library. Experiments on up to 14,336 cores run a set of representative analytics parallelized with TBB coupled with the hybrid MPI+TBB ExaStamp molecular dynamics code. TINS shows up to 40% performance improvement over various other approaches including the standard helper core.

We present a series of molecular dynamics simulations of the shock compression of copper matrices containing a single graphite inclusion: these model systems can be related to some specific carbon-rich rocks which, after a meteoritic impact, are found to contain small fractions of nanodiamonds embedded in graphite in the vicinity of high impedance minerals. We show that the graphite to diamond transformation occurs readily for nanometer-sized graphite inclusions, via a shock accumulation process, provided the pressure threshold of the bulk graphite/diamond transition is overcome, independently of the shape or size of the inclusion. Although high diamond yields (∼80%) are found after a few picoseconds in all cases, the transition is non-isotropic and depends substantially on the relative orientation of the graphite stack with respect to the shock propagation, leading to distinct nucleation processes and size-distributions of the diamond grains. A substantial regraphitization process occurs upon release and only inclusions with favorable orientations likely lead to the preservation of a fraction of this diamond phase. These results agree qualitatively well with the recent experimental observations of meteoritic impact samples.

Modern parallel architectures require applications to generate massive parallelism so as to feed their large number of cores and their wide vector units. We revisit the extensively studied classical Molecular Dynamics N-body problem in the light of these hardware constraints. We use Adaptive Mesh Refinement techniques to store particles in memory, and to optimize the force computation loop using multi-threading and vectorization-friendly data structures. Our design is guided by the need for load balancing and adaptivity raised by highly dynamic particle sets, as typically observed in simulations of strong shocks resulting in material micro-jetting. We analyze performance results on several simulation scenarios, over nodes equipped by Intel Xeon Phi Knights Landing (KNL) or Intel Xeon Skylake (SKL) processors. Performance obtained with our OpenMP implementation outperforms state-of-the-art implementations (LAMMPS) on both steady and micro-jetting particles simulations. In the latter case, our implementation is 4.7 times faster on KNL, and 2 times faster on SKL.