Julia parallel computing in IPython Jupyter

Question

I'm preparing a small presentation in Ipython where I want to show how easy it is to do parallel operation in Julia.

It's basically a Monte Carlo Pi calculation described here

The problem is that I can't make it work in parallel inside an IPython (Jupyter) Notebook, it only uses one.

I started Julia as: julia -p 4

If I define the functions inside the REPL and run it there it works ok.

@everywhere function compute_pi(N::Int)
    """
    Compute pi with a Monte Carlo simulation of N darts thrown in [-1,1]^2
    Returns estimate of pi
    """
    n_landed_in_circle = 0  
    for i = 1:N
        x = rand() * 2 - 1  # uniformly distributed number on x-axis
        y = rand() * 2 - 1  # uniformly distributed number on y-axis

        r2 = x*x + y*y  # radius squared, in radial coordinates
        if r2 < 1.0
            n_landed_in_circle += 1
        end
    end
    return n_landed_in_circle / N * 4.0    
end

 

function parallel_pi_computation(N::Int; ncores::Int=4)
    """
    Compute pi in parallel, over ncores cores, with a Monte Carlo simulation throwing N total darts
    """
    # compute sum of pi's estimated among all cores in parallel
    sum_of_pis = @parallel (+) for i=1:ncores
        compute_pi(int(N/ncores))
    end

    return sum_of_pis / ncores  # average value
end

 

julia> @time parallel_pi_computation(int(1e9))
elapsed time: 2.702617652 seconds (93400 bytes allocated)
3.1416044160000003

But when I do:

 using IJulia
 notebook()

And try to do the same thing inside the Notebook it only uses 1 core:

In [5]:  @time parallel_pi_computation(int(10e8))
elapsed time: 10.277870808 seconds (219188 bytes allocated)

Out[5]:  3.141679988

So, why isnt Jupyter using all the cores? What can I do to make it work?

Thanks.

Answer 1

Using addprocs(4) as the first command in your notebook should provide four workers for doing parallel operations from within your notebook.