Kovtun Mikael, Lambros Eleftherios, Liu Aodong, Tang Diandong, Williams-Young David B, Li Xiaosong
Department of Chemistry, University of Washington Seattle, Washington 98115, United States.
Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory Berkeley, California 94720, United States.
J Chem Theory Comput. 2024 Sep 3. doi: 10.1021/acs.jctc.4c00843.
Numerical integration of the exchange-correlation potential is an inherently parallel problem that can be significantly accelerated by graphical processing units (GPUs). In this Letter, we present the first implementation of GPU-accelerated exchange-correlation potential in the GauXC library for relativistic, 2-component density functional theory. By benchmarking against copper, silver, and gold coinage metal clusters, we demonstrate the speed and efficiency of our implementation, achieving significant speedup compared to CPU-based calculations. One GPU card provides computational power equivalent to roughly 400 CPU cores in the context of this work. The speedup further increases for larger systems, highlighting the potential of our approach for future, more computationally demanding simulations. Our implementation supports arbitrary angular momentum basis functions, enabling the simulation of systems with heavy elements and providing substantial speedup to relativistic electronic structure calculations. This advancement paves the way for more efficient and extensive computational studies in the field of density functional theory.
交换相关势的数值积分本质上是一个并行问题，图形处理单元（GPU）可显著加速该过程。在本信函中，我们展示了在GauXC库中针对相对论性二分量密度泛函理论首次实现的GPU加速交换相关势。通过对铜、银和金币属团簇进行基准测试，我们证明了该实现的速度和效率，与基于CPU的计算相比实现了显著加速。在本工作中，一块GPU卡提供的计算能力大致相当于400个CPU核心。对于更大的系统，加速效果进一步提升，凸显了我们的方法在未来更具计算挑战性的模拟中的潜力。我们的实现支持任意角动量基函数，能够模拟含有重元素的系统，并为相对论电子结构计算提供大幅加速。这一进展为密度泛函理论领域更高效、更广泛的计算研究铺平了道路。
