Slow down when running several parallel julia processes which use BLAS (MWE is provided)

General Usage Performance
4

Ok. Here is a simple MWE to test this out.

Create a file “task1.jl” with the following code

Base.BLAS.set_num_threads(1)
@inline blas_A_mul_B!(α::T, A::Matrix{T}, B::Vector{T}, β::T, C::Vector{T}) where T<:Number = Base.BLAS.gemv!('N', α, A, B, β, C)

srand(1234)

const D = 720
const A = rand(D,D)

function test_matvec(IM::Matrix{Float64}, N::Int64)
    x = rand(size(IM,1))
    y = rand(size(IM,1))
    t1 = time()
    @inbounds for i=1:N
        blas_A_mul_B!(one(Float64), IM, x, zero(Float64), y)
    end
    return time()-t1
end

test_matvec(A, 1)

t = test_matvec(A,500000)

io = open("out1.txt","w")
print(io, "$t\n")
close(io)

After that run in the bash

for i in `seq 2 4`; do cp task1.jl "task$i.jl"; sed -i -e "s/out1/out$i/g" "task$i.jl";  done

You can replace 4 with the number of processes you want.

After that I can start one process with julia task1.jl.
Or I can start all 4 processes in parallel by

for i in `seq 1 4`; do julia "task$i.jl" & done

In the former case, observing the file “out1.txt” reveals execution time of approximately 36 seconds on my machine.
In the latter case I got approximately 250 seconds in each of out… files.