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Meeting 2020 TMS Annual Meeting & Exhibition
Symposium Algorithm Development in Materials Science and Engineering
Presentation Title A Multi-GPU Implementation of a Full-field Crystal Plasticity Solver for Efficient Modeling of High-resolution Microstructures
Author(s) Adnan Eghtesad, Kai Germaschewski, Ricardo A Lebensohn, Marko Knezevic
On-Site Speaker (Planned) Adnan Eghtesad
Abstract Scope In this research, we present a high-performance implementation of a full-field elasto-visco-plastic fast Fourier transform (EVPFFT) crystal plasticity solver to take the advantage of graphics processing units (GPUs) across nodes of a supercomputer. To this end, the implementation combines the OpenACC programming model for GPU acceleration with MPI for distributed computing. Moreover, the FFT calculations are performed using the efficient Compute Unified Device Architecture (CUDA) FFT library, called CUFFT. Finally, to maintain performance portability, OpenACC-CUDA interoperability for data transfers between CPU and GPUs is used. The overall implementations are termed ACC-EVPCUFFT for single GPU and MPI-ACC-EVPCUFFT for multiple GPUs. To facilitate performance evaluation studies of the developed computational framework, deformation of a single phase copper is simulated, while to further demonstrate utility of the implementation for resolving fine microstructures, deformation of a dual-phase steel DP590 is simulated. The implementations and results are presented and discussed here in.
Proceedings Inclusion? Planned: Supplemental Proceedings volume

OTHER PAPERS PLANNED FOR THIS SYMPOSIUM

A First Principles Multi-cell Monte Carlo Method for Phase Prediction
A Multi-GPU Implementation of a Full-field Crystal Plasticity Solver for Efficient Modeling of High-resolution Microstructures
A New Phase-field Model with Anisotropic Interface Width for the Highly Anisotropic Growth of Ice Dendrites
A Self-consistent Parametric Homogenization Framework for Fatigue in Ni-based Superalloys
Advances in a Phase Field Dislocation Dynamics Model to Account for Various Gamma-surfaces of Hexagonal Close Packed Crystallography
Advancing Methods for Atomic-scale Modeling of Heterogeneous Systems
An Active Learning Approach for the Generation of Force Fields from DFT Calculations
An Atomistic Framework to Understand Solute Grain Boundary Segregation in a Polycrystal
Applying Machine Learning to Identifying Packing Defects in Amorphous Materials
Boosting the CALPHAD Modeling of Multi-component Systems by ab initio Calculations: Selected Case Studies
Bridging the Electronic, Atomistic and Mesoscopic Scales using Machine Learning
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Development of an Evolutionary Deep Neural Net for Materials Research
Direct Consideration of Vacancies in CALPHAD Modelling of Zirconium Carbide
Functional Uncertainty Propagation with Bayesian Ensembles in Molecular Dynamics
Generative Deep Neural Networks for Inverse Materials Design using Backpropagation and Adaptive Learning
Hierarchical Integration of Atomistically-derived Dislocation Mobility Laws into Discrete Dislocation Dynamics Simulations
High-throughput Computational Design of Organic-inorganic Hybrid Halide Semiconductors Beyond Perovskites
Interatomic Potentials as Physically-informed Artificial Neural Networks
Inverse Solutions Based on Reduced-order Process-structure-property Linkages Using Markov Chain Monte Carlo Sampling Algorithms
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L-1 (Digital): Machine Learning and Computer Vision on Classification of Carbon Nanotube and Nanofiber Structures for TEM Dataset
L-10: PyMob: Software for Automated Assessment of Atomic Mobilities
L-11: Randomness at Scale: Properties of Bulk Nanostructured Materials from Stochastic Representative Volume Elements
L-12: Simulation of Compressive Stress-strain Curve for Additive Manufactured Ti6Al4V Cuboctahedron Cellular Structure
L-13: Three-dimensional Modeling of Growth and Motion of Dendrites under Thermosolutal Convection
L-14: Uncertainty Propagation in CalPhaD Calculations
L-2 (Invited): Multi-Scale Modelling and Defect Engineering in Boron Carbon-Nitride van der Waals Heterostructures
L-3: An Improved Collocation Method to Treat Traction-free Surfaces in Dislocation Dynamics Simulations
L-4: Classifying Atomic Environments by the Gromov-Wasserstein Distance
L-5: Coupled Light Capture and Lattice Boltzmann Model of TiO2 Micropillars Array for Water Purification
L-6: Investigation of Fe-O and Fe-N and H-O Bond Formation Process by the Molecular Dynamics Simulations
L-7: Machine Learning Driven Functionally Graded Material Designs for Mitigation of Thermally Induced Stress
L-8: Methods to Simulate Grain Boundary Diffusion in Bicrystals and Polycrystals
L-9: Numerical Simulation for Microstructural Evolution in Solidification Process using CFD-CA (Cellular Automata) Coupled Method
Large Scale 3D Phase-field Sintering Simulations
Machine-learned Interatomic Potentials for Alloy Modeling and Phase Diagrams
Machine Learning Approaches for Improving Density Functional Tight Binding Models of Reactive Materials: Application to Astrobiolgical Materials and Surface Chemistry
Machine Learning Exploration and Optimization of Flame Spray Pyrolysis
Material Parameters Identification, Modeling and Experimental Verification of the New Smart Material Vacuum Packed Particles
MEAM-BO: Extension of MEAM to Include Bond Order for Polymer
Microstructure Image Analysis using Deep Convolutional Neural Networks
Microstructure Reconstruction of Additive Manufactured Metallic Materials with Markov Random Fields
Molecular Simulations You Can Trust and Reproduce: the OpenKIM Framework
Monte Carlo Study of Paired-spin Kagome Artificial Spin Ice Lattices
Multi-scale Modeling of Solidification Microstructure during Powder Bed Fusion
Multi-scale Modelling of Coarsening Process in the Ag-Cu Alloy
New Workflow for High-throughput Feature Extraction of Deforming Open Cell Foams
Nudged Elastic Band Method for Solid-solid Transition Under Finite Deformation
Persistent Homology: Unveiling the Topological Features in Materials Data
Phase Field Modeling of Microstructure Evolution During Selective Laser Sintering and Post Aging
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Quasiparticle Approach to Study Solute Segregation at Tilt grain Boundaries in Bcc Iron
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Robust and Accurate Self-consistent Homogenization of Elasto-viscoplastic Polycrystals
Scale Bridging from DFT to MD with Machine Learning
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