Julia is particularly attractive for its ability to run in parallel, not only on clusters, but also on the multiple cores of your laptop processor. Here is a minimalist example using pmap().
# For parallel computing with julia -p NumberOfAdditionalCPU @everywhere begin # this part will be available on all CPUs function computation(runid_is_useless) result = rand() return result end # function computation() end # @everywhere ### Main part - runs only on main instance of Julia. Nprocs = nprocs() print("Number of processors ",Nprocs,".\n") ## Launching computations on Nprocs parallel processors results = @time pmap(computation,1:Nprocs) ## Post-processing for i in 1:Nprocs print(@sprintf("Result of CPU %i is %f\n",i,results[i])) end # for i
On my laptop, my processor has 4 cores, and I run this code with julia -p 3 code.jl which gives
Number of processors 4. 0.548238 seconds (214.75 k allocations: 9.788 MB, 5.76% gc time) Result of CPU 1 is 0.725449 Result of CPU 2 is 0.377958 Result of CPU 3 is 0.443606 Result of CPU 4 is 0.016641
The same code with -p 39 can also be run on the 40 CPUs cluster of my department! For more material on parallel computing with Julia, you may take a look at Julia documentation.