Large scale ab initio calculations based on three levels of parallelization

We suggest and implement a parallelization scheme based on an efficient multiband eigenvalue solver, called the locally optimal block preconditioned conjugate gradient LOBPCG method, and using an optimized three-dimensional (3D) fast Fourier transform (FFT) in the ab initio}plane-wave code ABINIT. In addition to the standard data partitioning over processors corresponding to different k-points, we introduce data partitioning with respect to blocks of bands as well as spatial partitioning in the Fourier space of coefficients over the plane waves basis set used in ABINIT. This k-points-multiband-FFT parallelization avoids any collective communications on the whole set of processors relying instead on one-dimensional communications only. For a single k-point, super-linear scaling is achieved for up to 100 processors due to an extensive use of hardware optimized BLAS, LAPACK, and SCALAPACK routines, mainly in the LOBPCG routine. We observe good performance up to 200 processors. With 10 k-points our three-way data partitioning results in linear scaling up to 1000 processors for a practical system used for testing.Comment: 8 pages, 5 figures. Accepted to Computational Material Scienc

The Abinit project: Impact, environment and recent developments

International audienceAbinit is a material- and nanostructure-oriented package that implements density-functional theory (DFT) and many-bodyperturbation theory (MBPT) to find, from first principles, numerous properties including total energy, electronic structure, vibrational and thermodynamic properties, different dielectric and non-linear optical properties, and related spectra. In the special issue to celebrate the 40th anniversary of CPC, published in 2009, a detailed account of Abinit was included [Gonze et al, Comput. Phys. Comm. 180, 2582 (2009)], and has been amply cited. The present article comes as a follow-up to this 2009 publication. It i ncludes an analysis of the impact that Abinit has had, through for example the bibliometric indicators of the 2009 publication. Links with several other computational materials science projects are described. This article also covers the new capabilities of Abinit that have been implemented during the last three years, complementing a recent update of the 2009 article published in 2016. Physical and technical developments inside the abinit application are covered, as well as developments provided with the Abinit package, such as the multibinit and a-tdep projects, and related Abinit organization developments such as AbiPy . The new developments are described with relevant references, input variables, tests, and tutorials