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We present a data-parallel software package for fitting GaussianApproximation Potentials (GAPs) on multiple nodes using the ScaLAPACK librarywith MPI and OpenMP. Until now the maximum training set size for GAP models hasbeen limited by the available memory on a single compute node. In our newimplementation, descriptor evaluation is carried out in parallel with nocommunication requirement. The subsequent linear solve required to determinethe model coefficients is parallelised with ScaLAPACK. Our approach scales tothousands of cores, lifting the memory limitation and also deliveringsubstantial speedups. This development expands the applicability of the GAPapproach to more complex systems as well as opening up opportunities forefficiently embedding GAP model fitting within higher-level workflows such ascommittee models or hyperparameter optimisation.

Massively Parallel Fitting of Gaussian Approximation Potentials