Switch VecOfArrays for RecursiveArrayTools's VectorOfArray
#2491
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vchuravy
reviewed
Jul 22, 2025
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| mpi_neighbor_interfaces::VectorOfArray{Int, 2, Vector{Vector{Int}}} | ||
| mpi_neighbor_mortars::VectorOfArray{Int, 2, Vector{Vector{Int}}} | ||
| mpi_send_buffers::VectorOfArray{uEltype, 2, Vector{Vector{uEltype}}} | ||
| mpi_recv_buffers::VectorOfArray{uEltype, 2, Vector{Vector{uEltype}}} |
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This seems counter what I expect. For GPU support the send and receive buffers likely need to be CuArrays.
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Shouldn't they get Adapt.jled as well?
julia> mpicache = Trixi.P4estMPICache(Float32)
julia> typeof(mpicache.mpi_send_buffers)
RecursiveArrayTools.VectorOfArray{Float32, 2, Vector{Vector{Float32}}}
julia> mpicache.mpi_send_buffers = VectorOfArray(Vector{Vector{Float32}}(undef, 10))
VectorOfArray{Float32,2}:
10-element Vector{Vector{Float32}}:
#undef
#undef
#undef
#undef
#undef
#undef
#undef
#undef
#undef
#undef
julia> for index in 1:length(mpicache.mpi_send_buffers)
mpicache.mpi_send_buffers.u[index] = Vector{Float32}(undef, 10)
end
julia> Adapt.adapt_structure(CuArray, mpicache.mpi_send_buffers)
VectorOfArray{Float32,2}:
10-element Vector{CuArray{Float32, 1, CUDA.DeviceMemory}}:There was a problem hiding this comment.
Likely! Are we missing a rule?
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I think this is now doing what we expect:
julia> using Trixi, CUDA
julia> trixi_include("../examples/p4est_3d_dgsem/elixir_advection_basic.jl")
julia> semi_adapted = Trixi.trixi_adapt(CuArray, Float32, semi)
julia> typeof(semi.cache.fstar_primary_threaded)
RecursiveArrayTools.VectorOfArray{Float64, 5, Vector{Array{Float64, 4}}}
julia> typeof(semi_adapted.cache.fstar_primary_threaded)
RecursiveArrayTools.VectorOfArray{Float32, 5, Vector{CuArray{Float32, 4, CUDA.DeviceMemory}}}Should we try to merge @vchuravy ?
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DanielDoehring
added
refactoring
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Recent updates made RecursiveArrayTools's VectorOfArray do the right thing when used with Adapt.jl. Hence we can avoid using our own VecOfArrays.