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@simonbyrne
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# This file is machine-generated - editing it directly is not advised
[[Base64]]
uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
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using Statistics, MPI, Printf
MPI.Init()
struct SummaryStat
mean::Float64
var::Float64
n::Float64
end
function SummaryStat(X::Vector)
m = mean(X)
v = varm(X,m, corrected=false)
n = length(X)
SummaryStat(m,v,n)
end
function combine(S1::SummaryStat, S2::SummaryStat)
n = S1.n + S2.n
m = (S1.mean*S1.n + S2.mean*S2.n) / n
v = (S1.n * (S1.var + S1.mean * (S1.mean-m)) + S2.n * (S2.var + S2.mean * (S2.mean-m)))/n
SummaryStat(m,v,n)
end
function main(N)
comm = MPI.COMM_WORLD
isroot = MPI.Comm_rank(comm) == 0
X = rand(100,100)
# check we're computing the correct result
recv = MPI.Reduce(mapslices(SummaryStat, X, dims=1), combine, 0, comm)
Y = isroot ? zeros(100,100,MPI.Comm_size(comm)) : nothing
MPI.Gather!(X, Y, 0, comm)
if isroot
@assert [r.var for r in recv] var(Y; dims=(1,3), corrected=false)
end
T = zeros(N)
MPI.Barrier(comm)
for i = 1:N
T[i] = @elapsed(recv = MPI.Reduce(mapslices(SummaryStat, X, dims=1), combine, 0, comm))
MPI.Barrier(comm)
end
if MPI.Comm_rank(comm) == 0
@printf "nranks: %4d, niters: %4d, time (min/avg/max): %10.8f / %10.8f/ %10.8f\n" MPI.Comm_size(comm) N minimum(T) mean(T) maximum(T)
end
end
main(100)
using Statistics, MPI, Printf
MPI.Init()
struct SummaryStat
mean::Float64
var::Float64
n::Float64
end
function SummaryStat(X::Vector)
m = mean(X)
v = varm(X,m, corrected=false)
n = length(X)
SummaryStat(m,v,n)
end
function combine(S1::SummaryStat, S2::SummaryStat)
n = S1.n + S2.n
m = (S1.mean*S1.n + S2.mean*S2.n) / n
v = (S1.n * (S1.var + S1.mean * (S1.mean-m)) + S2.n * (S2.var + S2.mean * (S2.mean-m)))/n
SummaryStat(m,v,n)
end
function main(N)
comm = MPI.COMM_WORLD
X = randn(100)
recv = MPI.Reduce(SummaryStat(X), combine, 0, MPI.COMM_WORLD)
T = zeros(N)
MPI.Barrier(comm)
for i = 1:N
T[i] = @elapsed(recv = MPI.Reduce(SummaryStat(X), combine, 0, MPI.COMM_WORLD))
MPI.Barrier(comm)
end
if MPI.Comm_rank(comm) == 0
@printf "nranks: %4d, niters: %4d, time (min/avg/max): %10.8f / %10.8f/ %10.8f\n" MPI.Comm_size(comm) N minimum(T) mean(T) maximum(T)
end
end
main(100)
[deps]
MPI = "da04e1cc-30fd-572f-bb4f-1f8673147195"
Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
#!/bin/bash
#SBATCH --time=00:10:00
module load openmpi/4.0.3 julia/1.4.2
julia --project -e 'using Pkg; pkg"instantiate"; pkg"precompile"'
for ((n=1;n<=SLURM_JOB_NUM_NODES;n=n*2)); do
mpiexec -n $n julia --project mpi-variance-array.jl
done
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