4.5 Article

GPU_PBTE: an efficient solver for three and four phonon scattering rates on graphics processing units

Journal

JOURNAL OF PHYSICS-CONDENSED MATTER
Volume 33, Issue 49, Pages -

Publisher

IOP PUBLISHING LTD
DOI: 10.1088/1361-648X/ac268d

Keywords

phonon Boltzmann transport equation; graphics processing units; thermal conductivity; phonon scattering

Funding

  1. National Natural Science Foundation of China [51706134]
  2. Center for High Performance Computing at Shanghai Jiao Tong University

Ask authors/readers for more resources

This paper introduces a new algorithm optimized for calculating lattice thermal conductivity on GPUs, significantly improving double precision performance by two to three orders of magnitude compared to CPU. A new open-source code, GPU_PBTE, has been developed for studying the thermal transport properties of materials.
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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.5
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available