Abstract

Based on the muti-component eutectic multi-phase field model of S. G. Kim, W. T. Kim, T. Suzuki et al. [J. Cryst. Growth 261, 135–158 (2004)], the high-performance computing method of hardware and software architecture of OpenCL + graphics processing unit (GPU) was studied. Taking CBr4–C2Cl6 as an example, the evolution process of large-scale three-dimensional eutectic structure growth is realized by concurrent execution of multiple processes and multiple threads on two heterogeneous platforms of AMD and NVIDIA, respectively. The effects of different initial lamellar spacing and flow on eutectic lamellar morphology were also studied. The results show that, with the increasing of eutectic lamellar spacing, the morphology of eutectic lamellar changes are as follows: the symmetrically steady-state perpendicular to the interface is based on growth, the slightly oscillating state is unstable, and the large oscillating state is unstable; Under the condition of forced convection, the symmetrically alternating growth pattern of the original eutectic lamellar was broken, the melt flow led to the change of eutectic growth morphology, and the eutectic lamellar grew in the opposite direction of the flow. At the same computing scale, compared with the serial algorithm on the central processing unit platform, the acceleration ratio on the single GPU on the heterogeneous platform reaches 24.3 times and 21.6 times respectively, which improves the computing efficiency. At the same time, with its strong floating-point computing power to obtain more accurate simulation results and achieve the dual needs of computational efficiency and portability, it has also proven to solve the problems of a large amount of calculation, low efficiency and limited to qualitative research existing in the traditional phase-field models.

Highlights

  • The thermodynamic modeling and simulation of the solidification process and its microstructure is a mainstream trend in the development of solidification science.[1]

  • The results show that, with the increasing of eutectic lamellar spacing, the morphology of eutectic lamellar changes are as follows: the symmetrically steady-state perpendicular to the interface is based on growth, the slightly oscillating state is unstable, and the large oscillating state is unstable; Under the condition of forced convection, the symmetrically alternating growth pattern of the original eutectic lamellar was broken, the melt flow led to the change of eutectic growth morphology, and the eutectic lamellar grew in the opposite direction of the flow

  • During the whole solidification process of eutectic structure, the distribution of solute at the front of solid-liquid interface, the distance between eutectic layers and the presence of convection all have a great influence on the growth morphology of eutectic structure, affecting the properties of eutectic

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Summary

INTRODUCTION

Scitation.org/journal/adv limited computing area are faced.[7–10] At present, the performance of a single serial computer cannot meet the requirement of real-time. With 4000 gpus and 16 000 CPU cores, the computing performance reached 1.017 PFLOPS when the grid number was 1096 ∗ 6500 ∗ 10 400.13 At present, there are few cases in China that use GPU to solve the phase field model, but some scholars have studied it. The parallel implementation of serial multi-field coupling 3d phase field model on GPU has started and developed in China and abroad, but CUDA programming model is mostly adopted, aiming at a single hardware platform, and there is no research on the parallel algorithm of cross platform portability. This paper uses KKSM multiphase field model, taking CBr4–C2Cl6 as an example, to solve the problems of large calculation amount with low calculation efficiency, small simulation scale and code portability on heterogeneous platform

Phase field model and equations
Flow model and equation
PF-LBM model and equation
Alloy system settings
Physical properties of alloys
Boundary condition
DDR3 2 GB 32 kB
Experimental environment
Experimental results and analysis
Simulation-time analysis
AMD platform parallel algorithm test
CONCLUSION
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