多 GPU RI-HF 能量和分析梯度--面向高通量 Ab Initio 分子动力学。

IF 5.7 1区 化学 Q2 CHEMISTRY, PHYSICAL Journal of Chemical Theory and Computation Pub Date : 2024-09-10 Epub Date: 2024-08-27 DOI:10.1021/acs.jctc.4c00877
Ryan Stocks, Elise Palethorpe, Giuseppe M J Barca
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摘要

本文介绍了一种使用多个图形处理器(GPU)计算哈特里-福克分辨率(RI-HF)能量和分析梯度的优化算法和实现方法。该算法专为中小型分子(10-100 个原子)的高通量 ab initio 分子动力学模拟而设计。这项工作的主要创新包括利用多 GPU 并行性和工作负载平衡方案,在 GPU 之间有效地分配计算任务。我们的实现还采用了对称性利用、积分筛选和利用稀疏性优化内存使用的技术。计算结果表明,与之前的 GPU 加速 RI-HF 和传统高频方法相比,该实现方法实现了显著的性能提升,包括单 GPU AIMD 吞吐量提升了 3 倍以上。此外,当额外的 GPU 总内存允许存储解压缩的三中心积分时,利用多个 GPU 可以实现超线性加速。
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Multi-GPU RI-HF Energies and Analytic Gradients─Toward High-Throughput Ab Initio Molecular Dynamics.

This article presents an optimized algorithm and implementation for calculating resolution-of-the-identity Hartree-Fock (RI-HF) energies and analytic gradients using multiple graphics processing units (GPUs). The algorithm is especially designed for high throughput ab initio molecular dynamics simulations of small and medium size molecules (10-100 atoms). Key innovations of this work include the exploitation of multi-GPU parallelism and a workload balancing scheme that efficiently distributes computational tasks among GPUs. Our implementation also employs techniques for symmetry utilization, integral screening, and leveraging sparsity to optimize memory usage. Computational results show that the implementation achieves significant performance improvements, including over 3 × speedups in single GPU AIMD throughput compared to previous GPU-accelerated RI-HF and traditional HF methods. Furthermore, utilizing multiple GPUs can provide superlinear speedup when the additional aggregate GPU memory allows for the storage of decompressed three-center integrals.

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来源期刊
Journal of Chemical Theory and Computation
Journal of Chemical Theory and Computation 化学-物理:原子、分子和化学物理
CiteScore
9.90
自引率
16.40%
发文量
568
审稿时长
1 months
期刊介绍: The Journal of Chemical Theory and Computation invites new and original contributions with the understanding that, if accepted, they will not be published elsewhere. Papers reporting new theories, methodology, and/or important applications in quantum electronic structure, molecular dynamics, and statistical mechanics are appropriate for submission to this Journal. Specific topics include advances in or applications of ab initio quantum mechanics, density functional theory, design and properties of new materials, surface science, Monte Carlo simulations, solvation models, QM/MM calculations, biomolecular structure prediction, and molecular dynamics in the broadest sense including gas-phase dynamics, ab initio dynamics, biomolecular dynamics, and protein folding. The Journal does not consider papers that are straightforward applications of known methods including DFT and molecular dynamics. The Journal favors submissions that include advances in theory or methodology with applications to compelling problems.
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