Authors: V. Yamakov | Gabriele Jost | Daniel S. Kokron | Y. Mishin | Edward H. Glaessgen
Source: NASA STI Repository (National Aeronautics and Space Administration) (article, 2019-09-01)
Field: Materials Science — Materials Chemistry

High-Performance Computing Optimization for Aladyn – Adaptive Neural Network Molecular Dynamics Mini-Application This report provides a description and performance evaluation of the optimization techniques for high performance computing (HPC) implementation of the open source Computational Materials mini-application Aladyn (https://github.com/nasa/aladyn). Aladyn is a basic molecular dynamics code written in FORTRAN 2003, which is designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory (DFT) method. While achieving orders of magnitude faster computational performance than DFT, the ANN-based approach was still very computationally demanding compared to the conventional approach of using empirically fitted energy functions. After its initial development, Aladyn was evaluated and optimized by experts at the NASA Advanced Supercomputing (NAS) division to exploit modern supercomputer architectures. The code has been optimized for execution on multicore central processing units (CPUs), including Intel Skylake microarchitecture, and on graphic accelerators, such as Nvidia V100 graphic processing units (GPUs), using Open Multi-Processing (OpenMP) and Open Accelerators (OpenACC) programming interfaces. The optimization achieved a speedup of 4.7 times the baseline version on CPU performance and an additional 2.4 times on CPU+GPU performance.\n\n\n\n\nAtomistic computer simulations are a fundamental tool in materials research to model\nmaterial properties form physics-based first principles. Atomic interaction, governed by\nQuantum Mechanics (QM) require sophisticated and highly computationally demanding\nmathematical models to calculate [1]. Classical methods use approximate functional forms,\nempirically fitted through a set of variable parameters to emulate atomic energies as direct\nfunctions of atomic coordinates [2]. While empirical potentials are computationally much\nsimpler, allowing simulations of large-scale systems of up to a trillion (1012) atoms [3],\nthey are substantially less accurate compared to quantum calculations and applicable only\nto very specific atomic configurations or predefined crystallographic phases. A recently\nsuggested approach is to use heuristic machine learning methods [4], such as those based\non Adaptive Neural Networks (ANNs) to predict atomic energies, after being trained on a\nsufficiently large database of QM-calculated structures [5,6]. This approach reduces\nsignificantly the computational complexity, allowing for simulations of orders of\nmagnitude larger systems compared to QM-based methods without compromising\naccuracy. Still, compared to classical methods using empirical energy functions, ANN\nmethods remain two- to three orders of magnitude more computationally demanding.\nHence, the computational cost of simulations, together with the need for extensive training\nof ANNs, still makes the practical implementation of ANN-based methods quite\nchallenging.\nThe purpose of the Aladyn mini-application software [7], available as open source at https://github.com/nasa/aladyn, is to be a testbed for exploring possible optimization\nstrategies to develop highly scalable parallel algorithms for ANN-based atomistic\nsimulations. Aladyn is aimed at utilizing the architecture of the high-end modern highperformance\ncomputing (HPC) hardware based on multicore central processing units\n(CPUs) equipped with graphic processing unit (GPU) accelerators. Specifically, the goal\nis to optimize the performance on a single HPC compute node, before implementing\nscaling to multi-node parallelization using message passing interface (MPI). At the same\ntime, the open source code of Aladyn can serve as a training model for students and\nprofessors in academia. Computer science ; Supercomputer ; Speedup ; Fortran ; Computational science ; Parallel computing ; GPU cluster ; Multi-core processor ; Code (set theory) ; Central processing unit ; Adaptive mesh refinement ; Compiler ; Artificial neural network ; Set (abstract data type) ; CUDA ; Artificial intelligence ; Programming language Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Neural Networks and Applications