Real-Space Density Functional Theory on Graphical Processing Units: Computational Approach and Comparison to Gaussian Basis Set Methods
Real-Space Density Functional Theory on Graphical Processing Units: Computational Approach and Comparison to Gaussian Basis Set Methods
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DOI：
10.1021/ct400520e
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发表时间：
2013-10-01
影响因子：
5.5
通讯作者：
Aspuru-Guzik, Alan
中科院分区：
文献类型：
--
作者：
Andrade, Xavier;Aspuru-Guzik, Alan
我们讨论了图形处理单元(GPU)在加速实空间密度泛函理论(DFT)计算中的应用。为了使我们的实现高效，我们开发了一种方案来公开DFT方法中可用的数据并行性；这被应用于实空间DFT计算所需的不同程序。我们给出了AMD和NVIDIA的新一代GPU的结果，结果表明，我们的方案在免费代码Octopus中实现的单个GPU可以达到高达90G Flop的持续性能，与CPU版本的代码相比，速度有了显著的提高。此外，对于某些系统，我们的实现可以超越CPU高斯基集码，这表明实空间方法是在GPU上进行DFT模拟的一个有竞争力的替代方案。
We discuss the application of graphical processing units (GPUs) to accelerate real-space density functional theory (DFT) calculations. To make our implementation efficient, we have developed a scheme to expose the data parallelism available in the DFT approach; this is applied to the different procedures required for a real-space DFT calculation. We present results for current-generation GPUs from AMD and Nvidia, which show that our scheme, implemented in the free code Octopus, can reach a sustained performance of up to 90 GFlops for a single GPU, representing a significant speed-up when compared to the CPU version of the code. Moreover, for some systems, our implementation can outperform a CPU Gaussian basis set code, showing that the real-space approach is a competitive alternative for DFT simulations on GPUs.