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DArray: DaggerMPI for MPI support #407

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8 changes: 8 additions & 0 deletions lib/DaggerMPI/Project.toml
Original file line number Diff line number Diff line change
@@ -0,0 +1,8 @@
name = "DaggerMPI"
uuid = "37bfb287-2338-4693-8557-581796463535"
authors = ["Felipe de Alcântara Tomé <tomefelipe0@usp.br>", "Julian P Samaroo <jpsamaroo@jpsamaroo.me>"]
version = "0.1.0"

[deps]
Dagger = "d58978e5-989f-55fb-8d15-ea34adc7bf54"
MPI = "da04e1cc-30fd-572f-bb4f-1f8673147195"
100 changes: 100 additions & 0 deletions lib/DaggerMPI/src/DaggerMPI.jl
Original file line number Diff line number Diff line change
@@ -0,0 +1,100 @@
module DaggerMPI
using Dagger
import Base: reduce, fetch, cat
using MPI

export MPIBlocks

struct MPIBlocks{N} <: Dagger.AbstractSingleBlocks{N}
blocksize::NTuple{N, Int}
end
MPIBlocks(xs::Int...) = MPIBlocks(xs)

function Dagger.distribute(::Type{A},
x::Union{AbstractArray, Nothing},
dist::MPIBlocks,
comm::MPI.Comm=MPI.COMM_WORLD,
root::Integer=0) where {A<:AbstractArray{T, N}} where {T, N}
isroot = MPI.Comm_rank(comm) == root

# TODO: Make better load balancing

data = Array{T, N}(undef, dist.blocksize)
if isroot
cs = Array{T, N}(undef, size(x))
parts = partition(dist, domain(x))
idx = 1
for part in parts
cs[idx:(idx - 1 + prod(dist.blocksize))] = x[part]
idx += prod(dist.blocksize)
end
MPI.Scatter!(MPI.UBuffer(cs, div(length(cs), MPI.Comm_size(comm))), data, comm, root=root)
else
MPI.Scatter!(nothing, data, comm, root=root)
end

data = Dagger.tochunk(data)

return Dagger.DArray(T, domain(data), domain(data), data, dist)
end

function Dagger.distribute(::Type{A},
dist::MPIBlocks,
comm::MPI.Comm=MPI.COMM_WORLD,
root::Integer=0) where {A<:AbstractArray{T, N}} where {T, N}
return distribute(A, nothing, dist, comm, root)
end

function Dagger.distribute(x::AbstractArray,
dist::MPIBlocks,
comm::MPI.Comm=MPI.COMM_WORLD,
root::Integer=0)
return distribute(typeof(x), x, dist, comm, root)
end

function Base.reduce(f::Function, x::Dagger.DArray{T,N,MPIBlocks{N}};
dims=nothing,
comm=MPI.COMM_WORLD, root=nothing, acrossranks::Bool=true) where {T,N}
if dims === nothing
if !acrossranks
return fetch(Dagger.reduce_async(f,x))
elseif root === nothing
return MPI.Allreduce(fetch(Dagger.reduce_async(f,x)), f, comm)
else
return MPI.Reduce(fetch(Dagger.reduce_async(f,x)), f, comm; root)
end
else
if dims isa Int
dims = (dims,)
end
d = reduce(x.domain, dims=dims)
ds = reduce(x.subdomains[1], dims=dims)
if !acrossranks
thunks = Dagger.spawn(b->reduce(f, b, dims=dims), x.chunks[1])
return Dagger.DArray(T, d, ds, thunks, x.partitioning; concat=x.concat)
else
tmp = collect(reduce(f, x, comm=comm, root=root, dims=dims, acrossranks=false))
if root === nothing
h = UInt(0)
for dim in 1:N
if dim in dims
continue
end
h = hash(x.subdomains[1].indexes[dim], h)
end
h = abs(Base.unsafe_trunc(Int32, h))
newc = MPI.Comm_split(comm, h, MPI.Comm_rank(comm))
chunks = Dagger.tochunk(reshape(MPI.Allreduce(tmp, f, newc), size(tmp)))
else
rcvbuf = MPI.Reduce(tmp, f, comm; root)
if root != MPI.Comm_rank(comm)
return nothing
end
chunks = Dagger.tochunk(reshape(rcvbuf, size(tmp)))
end
return Dagger.DArray(T, d, ds, chunks, x.partitioning; concat=x.concat)
end
end
end

end # module