Using DFTK on GPUs

In this example we will look how DFTK can be used on Graphics Processing Units. In its current state runs based on Nvidia GPUs using the CUDA.jl Julia package are better supported and there are considerably less rough edges.

using AtomsBuilder
using DFTK
using PseudoPotentialData

Model setup. First step is to setup a Model in DFTK. This proceeds exactly as in the standard CPU case (see also our Tutorial).

silicon = bulk(:Si)

model  = model_DFT(silicon;
                   functionals=PBE(),
                   pseudopotentials=PseudoFamily("dojo.nc.sr.pbe.v0_4_1.standard.upf"))

Next is the selection of the computational architecture. This effectively makes the choice, whether the computation will be run on the CPU or on a GPU.

Nvidia GPUs. Supported via CUDA.jl. If you install the CUDA package, all required Nvidia cuda libraries will be automatically downloaded. So literally, the only thing you have to do is:

using CUDA
architecture = DFTK.GPU(CuArray)

DFTK.GPU{CUDA.CuArray}()

AMD GPUs. Supported via AMDGPU.jl. Here you need to install ROCm manually. With that in place you can then select:

using AMDGPU
architecture = DFTK.GPU(ROCArray)

DFTK.GPU{AMDGPU.ROCArray}()

Portable architecture selection. To make sure this script runs on the github CI (where we don't have GPUs available) we check for the availability of GPUs before selecting an architecture:

architecture = has_cuda() ? DFTK.GPU(CuArray) : DFTK.CPU()

DFTK.CPU()

Basis and SCF. Based on the architecture we construct a PlaneWaveBasis object as usual:

basis  = PlaneWaveBasis(model; Ecut=30, kgrid=(5, 5, 5), architecture)

... and run the SCF and some post-processing:

scfres = self_consistent_field(basis; tol=1e-6)
compute_forces(scfres)

2-element Vector{StaticArraysCore.SVector{3, Float64}}:
 [-1.543577034046708e-14, -1.3100040856417465e-14, -1.7936165441728804e-14]
 [1.5336087805413073e-14, 1.2525868139611569e-14, 1.8568244227832982e-14]