Julia parallel computing in IPython Jupyter - parallel-processing

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.

Answer 2

One way to solve this is to create a kernel that always uses 4 cores. For that some manual work is required. I assume that you are on a unix machine.
In the folder ~/.ipython/kernels/julia-0.x, you will find following kernel.json file:
{
"display_name": "Julia 0.3.9",
"argv": [
"/usr/local/Cellar/julia/0.3.9_1/bin/julia",
"-i",
"-F",
"/Users/ch/.julia/v0.3/IJulia/src/kernel.jl",
"{connection_file}"
],
"language": "julia"
}
If you copy the whole folder cp -r julia-0.x julia-0.x-p4, and modify the newly copied kernel.json file:
{
"display_name": "Julia 0.3.9 p4",
"argv": [
"/usr/local/Cellar/julia/0.3.9_1/bin/julia",
"-p",
"4",
"-i",
"-F",
"/Users/ch/.julia/v0.3/IJulia/src/kernel.jl",
"{connection_file}"
],
"language": "julia"
}
The paths will probably be different for you. Note that I only gave the kernel a new name and added the command line argument `-p 4.
You should see a new kernel named Julia 0.3.9 p4 which should always use 4 cores.
Also note that this kernel file will not get updated when you update IJulia, so you have to update it manually whenever you update julia or IJulia.

Answer 3

You can add new kernels using this command:
using IJulia
#for 4 cores
installkernel("Julia_4_threads", env=Dict("JULIA_NUM_THREADS"=>"4"))
#or for 8 cores
installkernel("Julia_8_threads", env=Dict("JULIA_NUM_THREADS"=>"8"))
After restart your VSCode this options will apear you your select kernel option.