Tersoff Multi-body Potential Research Articles

Thermal conductivity of monolayer hexagonal boron nitride: From defective to amorphous

Through extensive molecular dynamics simulations, we completed the thermal transport properties study of the monolayer hexagonal boron nitride (h-BN) films from the defect state to the amorphous state. To this end, a defective h-BN model construction program has been developed, which can customize the nature of the defects and realize the transition from the defect state to the amorphous state. By performing homogeneous non-equilibrium molecular dynamics simulations based on Tersoff multi-body potential, the thermal conductivity results of large-size h-BN films have been achieved. For the defect state h-BN, we studied the effect of different defect types and concentrations on its thermal conductivity and verified it from the phonon mechanism. After the transition to two-dimensional amorphous BN, we discussed and analyzed the influence of amorphous concentration, amorphous defect ratio and temperature on its thermal transport properties. On this basis, the restriction relationship between phonon-phonon scattering term and phonon-defect scattering term was also proposed from the atomic level mechanisms. Our results constitute a step in the deterministic engineering of thermal management devices in 2D materials, and hold great promise for the application of h-BN defect engineering in the field of nanomaterial thermal design.

Student cluster competition 2017, Team University of Texas at Austin/Texas State University: Reproducing vectorization of the Tersoff multi-body potential on the Intel Skylake and NVIDIA V100 architectures

This paper satisfies the reproducibility challenge of the Student Cluster Competition at Supercomputing 2017. We attempted to reproduce the results of H\"{o}hnerbach et al. (2016) for an implementation of a vectorized code for the Tersoff multi-body potential kernel of the molecular dynamics code Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS). We investigated accuracy, optimization performance, and scaling with our Intel CPU and NVIDIA GPU based cluster.

Student Cluster Competition 2017, Team University of Utah: Reproducing Vectorization of the Tersoff Multi-Body Potential on the Intel Broadwell and Intel Skylake Platforms

Abstract This paper describes the University of Utah SC17 Student Cluster Competition team’s efforts to reproduce the results of an SC16 paper titled “The Vectorization of the Tersoff Multi-Body Potential: An Exercise in Performance Portability”. This application optimizes the Tersoff potential within the widely-used molecular dynamics code LAMMPS. Tersoff multi-body potentials compute the force between two particles as a function of their distance and the relative position of surrounding particles. The application described in the cited paper focuses on providing node level and SIMD parallelization strategies for the Tersoff multi-body potential that are portable to different CPU instruction sets and to GPUs. In this paper we collect measurements on our SC17 cluster, which consists of a Broadwell head node and Skylake compute nodes. Our testing demonstrates that we can produce similar speedups to the original publication on the AVX2 instruction set on both the Broadwell and Skylake platforms. Additionally, we examined the performance on the Skylake node using the AVX-512 instruction set.

Student Cluster Competition 2017, Team Peking University: Reproducing vectorization of the Tersoff multi-body potential on the Intel Broadwell architecture

Höhnerbach et al. [1] proposed a general optimization method for the multi-body potential problem by exploiting vectorization on various of architectures using LAMMPS. As a task of the Student Cluster Competition in The International Conference for High Performance Computing, Networking, Storage and Analysis (SC17), we tried to reproduce the optimizations proposed in the paper. The molecular dynamics simulation of crystalline silicon is performed with LAMMPS on both our own single-node Intel Xeon machine and the cloud resource. The accuracy of the computation results is examined. The performance of the optimized program is compared with the original version to study the effectiveness and the scalability of the proposed optimization.

SC17 student cluster competition, Team Technical University of Munich and Friedrich–Alexander University Erlangen–Nürnberg: Reproducing vectorization of the Tersoff multi-body potential on the Intel Broadwell architecture

Abstract Markus Hohnerbach et al., describe optimizations for calculating the potential of a bond between two atoms that depends on their neighboring atoms in “The Vectorization of the Tersoff Multi-Body Potential: An Exercise in Performance Portability”. In this paper we present the results of our team’s effort to reproduce the claims made by Markus Hohnerbach et al., on an Intel Broadwell architecture at the SC17 Student Cluster Competition. We first demonstrate that these optimizations result in no relevant loss of precision using types with different floating point representations and then compare their respective runtime. Lastly, we show the scalability of the double precision version of the algorithm by using an increasing number of threads.

Student cluster competition 2017, team Northeastern University: Reproducing vectorization of the Tersoff multi-body potential on the NVIDIA V100

Abstract This paper evaluates the reproducibility of an efficient and portable Tersoff potential calculation using LAMMPS, previously presented at the Supercomputing ’16 Conference. The original artifact description was used to reproduce the results on an AMD EPYC 7551, equipped with NVIDIA Volta V100 GPUs. Molecular dynamics simulations, in particular multi-body potential problems, can provide increased accuracy and support scientific discovery in the fields of computational chemistry and materials science. This paper evaluates the reproducibility of the study, in terms of portability and performance.

Student cluster competition 2017, team NTHU: Reproducing vectorization of the tersoff multi-body potential on the Intel Skylake and Nvidia P100 architecture

Abstract Markus Hohnerbach et al. recently published a work to optimize the performance of Tersoff potential, which is a computing scheme used in the LAMMPS molecular dynamics (MD) code. The optimization solver was implemented with three different computation precisions, namely single, mixed, and double. As a special activity of the Student Cluster Competition at SC17 conference, we aimed to reproduce their experimental studies of the optimization solvers in terms of accuracy, performance and scalability. We conducted our experiments on a cluster with Intel Xeon Gold 6130 CPUs and Nvidia Tesla P100 GPUs, while the original work was on a cluster with Intel Xeon E5-2650 CPUs and K40 GPUs. Desite of the differences in computing systems, we demonstrate the claims from the original work can be successfully reproduced by showing the solvers with less precision implementation can still achieve high accuracy results, and exhibit good performance speedup and scalability.

Student cluster competition 2017, team Tsinghua University: Reproducing vectorization of the tersoff multi-body potential on the Intel Skylake and NVIDIA Volta architectures

Abstract A paper of SC ’16 entitled “The Vectorization of the Tersoff Multi-Body Potential: An Exercise in Performance Portability” Hohnerbach et al. (2016) [1] implemented reduced precision calculation and cross-platform vectorization for Tersoff potential, which the authors claimed as accurate, efficient, scalable and portable. In this report, we focus on recently released computing architectures, Intel Skylake and NVIDIA Volta, to present our results and compare them with the Tersoff paper. With new input provided by Porter et al. (1997) [2], we run the given testcases on our cluster and obtain results that not consistent with the performance improvements and scalability claimed in the original publication. Deeper analysis demonstrate that it is the communication bottleneck caused by special characteristics of the new input data that limit the reproducibility.

Student Cluster Competition 2017, team Nanyang Technological University: Reproducing vectorization of the Tersoff multi-body potential on the Intel Broadwell architecture

Abstract Reproducing previous work plays an important role in validating its effectiveness under different experiment settings. In this paper, we reproduce the results from the paper “The vectorization of the Tersoff multi-body potential: an exercise in performance portability” [1]. In particular, we reproduce the accuracy of the reduced precision solvers, the speed-up of the vectorized implementation, and the strong scaling test using Intel Xeon E5-2699 v4 Broadwell CPUs. We show that our reproduced results match the claims made in the original paper.