论文标题
量子扰动理论使用张量核心和深度神经网络
Quantum perturbation theory using Tensor cores and a deep neural network
论文作者
论文摘要
使用张量核进行时间非依赖性量子响应计算。这是通过将密度矩阵扰动理论映射到深神经网络的计算结构中来实现的。每个深层的主要计算成本以张量收缩为主，即密集的矩阵 - 矩阵乘积，以混合精度算术算法接近峰值性能。使用自洽的电荷密度函数紧密结合理论以及耦合扰动的Hartree-Fock理论来证明和分析量子响应计算。对于线性响应计算，提出了一种新颖的无参数收敛标准，非常适合数值嘈杂的低精度浮点操作，我们使用两个NVIDIA A100 GPU的张量核心证明了近200个TFLOPS的峰值性能。
Time-independent quantum response calculations are performed using Tensor cores. This is achieved by mapping density matrix perturbation theory onto the computational structure of a deep neural network. The main computational cost of each deep layer is dominated by tensor contractions, i.e. dense matrix-matrix multiplications, in mixed precision arithmetics which achieves close to peak performance. Quantum response calculations are demonstrated and analyzed using self-consistent charge density-functional tight-binding theory as well as coupled-perturbed Hartree-Fock theory. For linear response calculations, a novel parameter-free convergence criterion is presented that is well-suited for numerically noisy low precision floating point operations and we demonstrate a peak performance of almost 200 Tflops using the Tensor cores of two Nvidia A100 GPUs.
