Zhang Bo, Fan Zheyong, Zhao C Y, Gu Xiaokun
Institute of Engineering Thermophysics, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China.
School of Mathematics and Physics, Bohai University, Jinzhou, People's Republic of China.
J Phys Condens Matter. 2021 Sep 30;33(49). doi: 10.1088/1361-648X/ac268d.
Lattice thermal conductivity (LTC) is a key parameter for many technological applications. Based on the Peierls-Boltzmann transport equation (PBTE), many unique phonon transport properties of various materials were revealed. Accurate calculation of LTC with PBTE, however, is a time-consuming task, especially for compounds with a complex crystal structure or taking high-order phonon scattering into consideration. Graphical processing units (GPUs) have been extensively used to accelerate scientific simulations, making it possible to use a single desktop workstation for calculations that used to require supercomputers. Due to its fundamental differences from traditional processors, GPUs are especially suited for executing a large group of similar tasks with minimal communication, but require completely different algorithm design. In this paper, we provide a new algorithm optimized for GPUs, where a two-kernel method is used to avoid divergent branching. A new open-source code, GPU_PBTE, is developed based on the proposed algorithm. As demonstrations, we investigate the thermal transport properties of silicon and silicon carbide, and find that accurate and reliable LTC can be obtained by our software. GPU_PBTE performed on NVIDIA Tesla V100 can extensively improve double precision performance, making it two to three orders of magnitude faster than our CPU version performed on Intel Xeon CPU Gold 6248 @2.5 GHz. Our work also provides an idea of accelerating calculations with other novel hardware that may come out in the future.
晶格热导率（LTC）是许多技术应用中的关键参数。基于派尔斯 - 玻尔兹曼输运方程（PBTE），揭示了各种材料许多独特的声子输运特性。然而，用PBTE精确计算LTC是一项耗时的任务，特别是对于具有复杂晶体结构的化合物或考虑高阶声子散射的情况。图形处理单元（GPU）已被广泛用于加速科学模拟，使得使用单个桌面工作站就能进行过去需要超级计算机才能完成的计算。由于其与传统处理器存在根本差异，GPU特别适合执行大量通信最少的类似任务，但需要完全不同的算法设计。在本文中，我们提供了一种针对GPU优化的新算法，其中使用双内核方法来避免发散分支。基于所提出的算法开发了一个新的开源代码GPU_PBTE。作为示例，我们研究了硅和碳化硅的热输运特性，发现通过我们的软件可以获得准确可靠的LTC。在NVIDIA Tesla V100上运行的GPU_PBTE可以大幅提高双精度性能，使其比在英特尔至强CPU Gold 6248 @2.5 GHz上运行的CPU版本快两到三个数量级。我们的工作还为未来可能出现的其他新型硬件加速计算提供了思路。
