Open tmigot opened 2 months ago
I am wondering how much work it would be to make QN operators compatible with GPUs (CuArray for instance)?
Typically, my use case would be something like this
using CUDA, NLPModels, NLPModelsModifiers, NLPModelsTest V = CuArray{Float64} nlp = NLSLC(V) CUDA.allowscalar() list_QN = [LBFGSModel, LSR1Model, DiagonalPSBModel, DiagonalAndreiModel, SpectralGradientModel] lnlp = list[1](nlp) x = nlp.meta.x0 v = copy(x) Hv = similar(x) hprod!(lnlp, x, v, Hv)
These model modifiers internally call the QN operators from LinearOperators.jl, e.g. op = LBFGSOperator(T, nlp.meta.nvar).
op = LBFGSOperator(T, nlp.meta.nvar)
I am wondering how much work it would be to make QN operators compatible with GPUs (CuArray for instance)?
Typically, my use case would be something like this
These model modifiers internally call the QN operators from LinearOperators.jl, e.g.
op = LBFGSOperator(T, nlp.meta.nvar)
.