论文标题
高斯近似电势的大规模平行拟合
Massively Parallel Fitting of Gaussian Approximation Potentials
论文作者
论文摘要
我们提出了一个数据并行软件包，用于使用带有MPI和OpenMP的Scalapack库在多个节点上拟合高斯近似势（GAP）。到目前为止，GAP模型的最大训练集大小受到单个计算节点上的可用内存的限制。在我们的新实施中，描述符评估是并行进行的，没有任何通信要求。确定模型系数所需的随后的线性求解与Scalapack并行。我们的进近缩放到成千上万的内核，提高记忆限制并提供实质性的加速。这一发展扩大了GAP方法对更复杂的系统的适用性，并为将差距模型拟合有效地嵌入到诸如委员会模型或超参数优化之类的高级工作流程中。
We present a data-parallel software package for fitting Gaussian Approximation Potentials (GAPs) on multiple nodes using the ScaLAPACK library with MPI and OpenMP. Until now the maximum training set size for GAP models has been limited by the available memory on a single compute node. In our new implementation, descriptor evaluation is carried out in parallel with no communication requirement. The subsequent linear solve required to determine the model coefficients is parallelised with ScaLAPACK. Our approach scales to thousands of cores, lifting the memory limitation and also delivering substantial speedups. This development expands the applicability of the GAP approach to more complex systems as well as opening up opportunities for efficiently embedding GAP model fitting within higher-level workflows such as committee models or hyperparameter optimisation.
