Quantum perturbation theory using Tensor cores and a deep neural network

• URL: http://arxiv.org/abs/2203.09621v2
• Date: Tue, 10 May 2022 14:21:22 GMT
• Title: Quantum perturbation theory using Tensor cores and a deep neural network
• Authors: Joshua Finkelstein, Emanuel H. Rubensson, Susan M. Mniszewski, Christian F. A. Negre, Anders M. N. Niklasson
• Abstract summary: Time-independent quantum response calculations are performed using floating cores. We demonstrate a peak performance of almost 200 Tflops using the cores of two Nvidia A100 GPUs.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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