![\"The](\"http:\/\/djalil.chafai.net\/blog\/wp-content\/uploads\/2014\/04\/julia-300x202.png\")
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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 minimalistic example using pmap()<\/strong>.<\/p>\n On my laptop, my processor has 4 cores, and I run this code with julia -p 3 code.jl<\/strong> which gives<\/p>\n The same code with -p 39<\/strong> 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<\/a>.<\/p>\n","protected":false},"excerpt":{"rendered":" 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.…<\/p>\n# For parallel computing with julia -p NumberOfAdditionalCPU\r\n\r\n@everywhere begin # this part will be available on all CPUs\r\n\r\nfunction computation(runid_is_useless)\r\n result = rand()\r\n return result\r\nend # function computation()\r\n\r\nend # @everywhere\r\n\r\n### Main part - runs only on main instance of Julia.\r\n\r\nNprocs = nprocs()\r\nprint(\"Number of processors \",Nprocs,\".\\n\")\r\n\r\n## Launching computations on Nprocs parallel processors\r\nresults = @time pmap(computation,1:Nprocs)\r\n\r\n## Post-processing\r\nfor i in 1:Nprocs\r\n print(@sprintf(\"Result of CPU %i is %f\\n\",i,results[i]))\r\nend # for i\r\n<\/pre>\n
Number of processors 4.\r\n0.548238 seconds (214.75 k allocations: 9.788 MB, 5.76% gc time)\r\nResult of CPU 1 is 0.725449\r\nResult of CPU 2 is 0.377958\r\nResult of CPU 3 is 0.443606\r\nResult of CPU 4 is 0.016641\r\n<\/pre>\n