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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)
The text was updated successfully, but these errors were encountered:
hprod!
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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)
.The text was updated successfully, but these errors were encountered: