We present the details of a graphics processing unit (GPU) capable exchange correlation (XC) scheme integrated into the open source QUantum Interaction Computational Kernel (QUICK) program. Our implementation features an octree based numerical grid point partitioning scheme, GPU enabled grid pruning and basis and primitive function prescreening, and fully GPU capable XC energy and gradient algorithms. Benchmarking against the CPU version demonstrated that the GPU implementation is capable of delivering an impressive performance while retaining excellent accuracy. For small to medium size protein/organic molecular systems, the realized speedups in double precision XC energy and gradient computation on a NVIDIA V100 GPU were 60–80-fold and 140–500-fold, respectively, as compared to the serial CPU implementation. The acceleration gained in density functional theory calculations from a single V100 GPU significantly exceeds that of a modern CPU with 40 cores running in parallel.

中文翻译：

---

在量子相互作用计算内核程序中并行执行密度泛函理论方法。

我们将介绍集成到开源QUantum交互计算内核（QUICK）程序中的具有图形处理单元（GPU）功能的交换相关（XC）方案的详细信息。我们的实现功能包括基于八叉树的数字网格点分区方案，支持GPU的网格修剪和基础以及原始功能预筛选以及完全支持GPU的XC能量和梯度算法。根据CPU版本进行的基准测试表明，GPU实施能够提供令人印象深刻的性能，同时保持出色的精度。对于中小型蛋白质/有机分子系统，与串行CPU实施相比，在NVIDIA V100 GPU上实现的双精度XC能量和梯度计算的加速分别为60-80倍和140-500倍。