Accelerating solidification process simulation for large-sized system of liquid metal atoms using GPU with CUDA

Molecular dynamics simulation is a powerful tool to simulate and analyze complex physical processes and phenomena at atomic characteristic for predicting the natural time-evolution of a system of atoms. Precise simulation of physical processes has strong requirements both in the simulation size and computing timescale. Therefore, finding available computing resources is crucial to accelerate computation. However, a tremendous computational resource (GPGPU) are recently being utilized for general purpose computing due to its high performance of floating-point arithmetic operation, wide memory bandwidth and enhanced programmability. As for the most time-consuming component in MD simulation calculation during the case of studying liquid metal solidification processes, this paper presents a fine-grained spatial decomposition method to accelerate the computation of update of neighbor lists and interaction force calculation by take advantage of modern graphics processors units (GPU), enlarging the scale of the simulation system to a simulation system involving 10?000?000 atoms. In addition, a number of evaluations and tests, ranging from executions on different precision enabled-CUDA versions, over various types of GPU (NVIDIA 480GTX, 580GTX and M2050) to CPU clusters with different number of CPU cores are discussed. The experimental results demonstrate that GPU-based calculations are typically 9 ~ 11 times faster than the corresponding sequential execution and approximately 1.5 ~ 2 times faster than 16 CPU cores clusters implementations. On the basis of the simulated results, the comparisons between the theoretical results and the experimental ones are executed, and the good agreement between the two and more complete and larger cluster structures in the actual macroscopic materials are observed. Moreover, different nucleation and evolution mechanism of nano-clusters and nano-crystals formed in the processes of metal solidification is observed with large-sized system.

[1]  Tetsu Narumi,et al.  Accelerating molecular dynamics simulation using graphics processing unit , 2010 .

[2]  Zhao-Hui Jin,et al.  Glass transition and atomic structures in supercooled Ga0.15Zn0.15Mg0.7 metallic liquids: A constant pressure molecular dynamics study , 1997 .

[3]  B. Alder,et al.  Phase Transition for a Hard Sphere System , 1957 .

[4]  Weiguo Liu,et al.  Streaming Algorithms for Biological Sequence Alignment on GPUs , 2007, IEEE Transactions on Parallel and Distributed Systems.

[5]  Federico D. Sacerdoti,et al.  Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters , 2006, ACM/IEEE SC 2006 Conference (SC'06).

[6]  Wang,et al.  Anomalies in the structure factor for some rapidly quenched metals. , 1992, Physical review. B, Condensed matter.

[7]  S Wang,et al.  Structure and electrical resistivities of liquid binary alloys , 1980 .

[8]  Joshua A. Anderson,et al.  General purpose molecular dynamics simulations fully implemented on graphics processing units , 2008, J. Comput. Phys..

[9]  Weiguo Liu,et al.  Accelerating molecular dynamics simulations using Graphics Processing Units with CUDA , 2008, Comput. Phys. Commun..

[10]  Peter Schröder,et al.  Quantum Monte Carlo on graphical processing units , 2007, Comput. Phys. Commun..

[11]  Kwang Jin Oh,et al.  A parallel molecular dynamics simulation scheme for a molecular system with bond constraints in NPT ensemble , 2006, Comput. Phys. Commun..

[12]  Jeffrey S. Vetter,et al.  Quantifying NUMA and contention effects in multi-GPU systems , 2011, GPGPU-4.

[13]  S Wang,et al.  Variational thermodynamic calculation for simple liquid metals and alkali alloys , 1983 .

[14]  Laxmikant V. Kalé,et al.  Overcoming scaling challenges in biomolecular simulations across multiple platforms , 2008, 2008 IEEE International Symposium on Parallel and Distributed Processing.

[15]  Dong Ke Parallel algorithm of solidification process simulation for large-sized system of liquid metal atoms , 2003 .

[16]  Akihiko Hirata,et al.  Direct observation of local atomic order in a metallic glass. , 2011, Nature materials.

[17]  Steve Plimpton,et al.  Fast parallel algorithms for short-range molecular dynamics , 1993 .

[18]  Li-Xia Liu,et al.  Short-range and medium-range order in Ca7Mg3 metallic glass , 2010 .

[19]  Robert G. Belleman,et al.  High Performance Direct Gravitational N-body Simulations on Graphics Processing Units , 2007, ArXiv.

[20]  Klaus Schulten,et al.  Accelerating Molecular Modeling Applications with GPU Computing , 2009 .

[21]  Norbert Attig,et al.  Introduction to Molecular Dynamics Simulation , 2004 .

[22]  J. Banavar,et al.  Computer Simulation of Liquids , 1988 .

[23]  Vijay S. Pande,et al.  Accelerating molecular dynamic simulation on graphics processing units , 2009, J. Comput. Chem..

[24]  Yuefan Deng,et al.  An efficient parallel implementation of the smooth particle mesh Ewald method for molecular dynamics simulations , 2007, Comput. Phys. Commun..

[25]  Wang,et al.  Subpeaks of structure factors for rapidly quenched metals. , 1992, Physical review. B, Condensed matter.

[26]  Michael R. Macedonia,et al.  The GPU Enters Computing's Mainstream , 2003, Computer.

[27]  Wolfgang Paul,et al.  GPU accelerated Monte Carlo simulation of the 2D and 3D Ising model , 2009, J. Comput. Phys..

[28]  Amitabh Varshney,et al.  High-throughput sequence alignment using Graphics Processing Units , 2007, BMC Bioinformatics.

[29]  Yutaka Okabe,et al.  GPU-based Swendsen-Wang multi-cluster algorithm for the simulation of two-dimensional classical spin systems , 2012, Comput. Phys. Commun..

[30]  Xin Wang,et al.  Simulation study on the formation and evolution properties of nano-clusters in rapid solidification structures of sodium , 2007 .

[31]  R. C. Reeder,et al.  A Coarse Grain Model for Phospholipid Simulations , 2001 .

[32]  Yoshio Waseda,et al.  The structure of non-crystalline materials , 1980 .

[33]  Aibing Yu,et al.  Formation and description of nano-clusters formed during rapid solidification processes in liquid metals , 2005 .

[34]  Aoki Takayuki,et al.  Multi-GPU performance of incompressible flow computation by lattice Boltzmann method on GPU cluster , 2011, ParCo 2011.

[35]  S Wang,et al.  Variational calculation of Helmholtz free energies with applications to the sp-type liquid metals , 1986 .

[36]  Eric Darve,et al.  N-Body Simulations on GPUs , 2007, ArXiv.

[37]  David Defour,et al.  Line-by-line spectroscopic simulations on graphics processing units , 2008, Comput. Phys. Commun..

[38]  Paul S. Crozier,et al.  General-purpose molecular dynamics simulations on GPU-based clusters , 2011 .

[39]  Rainer Künnemeyer,et al.  Accelerating Monte Carlo simulations with an NVIDIA® graphics processor , 2009, Comput. Phys. Commun..

[40]  Long Chen,et al.  Dynamic load balancing on single- and multi-GPU systems , 2010, 2010 IEEE International Symposium on Parallel & Distributed Processing (IPDPS).

[41]  M. Januszewski,et al.  Accelerating numerical solution of stochastic differential equations with CUDA , 2009, Comput. Phys. Commun..

[42]  Jie Tian,et al.  GPU-based Monte Carlo simulation for light propagation in complex heterogeneous tissues. , 2010, Optics express.

[43]  Aibing Yu,et al.  Formation and magic number characteristics of clusters formed during solidification processes , 2007 .

[44]  H. Nakano,et al.  Variational calculations for metastable systems: Thermodynamic properties of glass transitions , 1989 .

[45]  Wei Ge,et al.  Petascale molecular dynamics simulation of crystalline silicon on Tianhe-1A , 2013, Int. J. High Perform. Comput. Appl..

[46]  Anqi Zou,et al.  Performance Analyses of a Parallel Verlet Neighbor List Algorithm for GPU-Optimized MD Simulations , 2012, 2012 ASE/IEEE International Conference on BioMedical Computing (BioMedCom).