1. “High-performance computational GPU-stand for teaching undergraduate and graduate students the basics of quantum-mechanical calculations“ “Komsomolsk-on-Amur State Technical University” 681013, Russia, Komsomolsk-on-Amur city, Lenina-27 email: gray@knastu.ru, uru.40@mail.ru,only_nina@mail.rugray@knastu.ruuru.40@mail.ru,only_nina@mail.ru Singapore-2013 Sergey Seriy

2. Overview  Teaching of classical and DFT “ab-initio” calculations( samples of bulk, slab, particle structures, thin-film, alloys, etc)  Using Atomistic Simulation Environment (ASE) and GPU-based software for calculations  ASE universality: electronic structure codes + LAMMPS or ABINIT or CP2K or GPAW, with GPU- support  ASE exercises: tests of defect energies, heats of formation, elastic constants, etc.  Conclusion

3. Main Goal: “Teaching for calculate basic structural properties of single crystals, formation energies of defects, and structural and elastic properties of simple nanostructures, using GPU (NVidia, ATI and both)” Need to learn formats of input parameter files, atomic conguration files, and output files of ab-initio code (CP2K, Abinit, GPAW) classical code (LAMMPS) Need to create atomic congurations for single crystal structures, crystallic compounds, point defects (vacancies, interstitials, substitutions), planar defects (varying surfaces, stacking faults), and strained structures

4. Need a tool applicable to quickly evaluate basic properties from classical potentials and ab-initio methods. Ideally, a single universal tool would be able to create basic atomic congurations and manipulate them serve these atomistic congurations as inputs to a variety of methods/simulation codes and obtain energies Anything like that available? Atomistic Simulation Environment (ASE)

5. Atomistic Simulation Environment (ASE) v. 3.0 universal Python interface to many DFT codes (calculators), with visualization, simple GUI, documentation, and tutorials creates molecules, crystal structures, surfaces, nanotubes, analyzes symmetry and spacegroups provides support for Equation of state, structure optimization,dissociation, diusion, constrains, NEB, vibration analysis, phonon calculations, infrared intensities, molecular dynamics, STM, electron transport, …

6. -- and CP2K – DFT-based molecular dynamics code (consist in old version ASE 2.0)

15. Conclusions  ASE provides a universal interface to many electronic- structure codes  ASE interface for LAMMPS, ABINIT and CP2K on GPU was utilized in learning students  Following the LAMMPS example, ASE can provide support to other classical and “ab-initio” codes  ASE simplies and increases eciency of atomistic simulation learning

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19. Multicore, CPU & GPU SpecificationsCore i7 960GTX285 Processing Elements 4 cores, 4 way SIMD @3.2 GHz 30 cores, 8 way SIMD @1.5 GHz Resident Strands/Threads (max) 4 cores, 2 threads, 4 way SIMD: 32 strands 30 cores, 32 SIMD vectors, 32 way SIMD: 30720 threads SP GFLOP/s1021080 Memory Bandwidth25.6 GB/s159 GB/s Register File-1.875 MB Local Store-480 kB Core i7 (45nm) GTX285 (55nm)

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27. Mathematical modeling: nano- coatings for cutting tools

28. VMD – “Visual Molecular Dynamics”  Visualization and analysis of molecular dynamics simulations, sequence data, volumetric data, quantum chemistry simulations, particle systems, …  User extensible with scripting and plugins

29. Molecular orbital calculation and display: factor of 120x faster Imaging of gas migration pathways in proteins with implicit ligand sampling: factor of 20x to 30x faster GPU Acceleration in VMD Electrostatic field calculation, ion placement: factor of 20x to 44x faster

30. Mesoscale modelling on CUDA: Fluid Dynamics Double precision 384 x 384 x 192 grid (max that fits in 4GB) Vertical slice of temperature at y=0 Transition from stratified (left) to turbulent (right) Regime depends on Rayleigh number: Ra = gαΔT/κν 8.5x speedup versus Fortran code running on 8- core 2.5 GHz Xeon

31. Thank you for your attention