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/))
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.