加速密度拟合的自适应精度与8位整数AI加速器
Accelerating Density Fitting with Adaptive-precision and 8-bit Integer on AI Accelerators
AI总结：
本文提出一种自适应精度算法，利用8位整数运算加速AI加速器上的密度拟合方法，实验证明在RTX 4090和RTX 6000上分别提升204%和364%的效率，同时保持收敛能量。
摘要：
人工智能（AI）加速器如NVIDIA Tensor Cores的出现为加速张量密集型科学计算提供了新机遇。然而，将其应用于量子化学存在严格精度要求和不规则数据模式的挑战。本文提出一种自适应精度算法，利用8位整数（INT8）运算加速基于Gaussian基组的密度拟合（DF）方法。该算法在GPU加速的PySCF包中实现，并在超过二十种分子系统上测试了不同NVIDIA GPU。与标准FP64代码相比，在RTX 4090游戏GPU上加速达204%，在RTX 6000 Ada工作站GPU上加速达364%，且不牺牲收敛能量。本工作展示了使用AI硬件进行可靠量子化学模拟的实用方法。
英文摘要：
The emergence of artificial intelligence (AI) accelerators like NVIDIA Tensor Cores offers new opportunities to speed up tensor-heavy scientific computations. However, applying them to quantum chemistry is challenging due to strict accuracy demands and irregular data patterns. We propose an adaptive precision algorithm to accelerate the density fitting (DF) method with Gaussian basis sets on AI accelerators using 8-bit integer (INT8) arithmetics. Implemented in the GPU-accelerated PySCF package, the algorithm is tested on more than twenty molecular systems with different NVIDIA GPUs. Compared to the standard FP64 code, our algorithm is up to 204\% faster on a RTX 4090 gaming GPU and up to 364\% faster on a RTX 6000 Ada workstation GPU without compromising the converged energy. This work demonstrates a practical approach to use AI hardware for reliable quantum chemistry simulations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
