macOS, julia 0.6.2, Feb 2018, captain’s log: I have been playing around with trying to understand (the docs on) parallelism. as an early example, I just want to create, in parallel, a list of function logp1(x::Int64)::Float64 log(1.0+x); end#function Because I want to check the result, I later a…

Don’t time in global scope, use a function instead. And use @btime of BenchmarkTools.jl to give more accurate benchmarking results. The above benchmarks are highly questionable. In global scope, variables are type unstable so you are timing the slow version of Julia. Also if you are running the func…

Ok now significantly less absurd: function pmap_sum() M = 1000000 r = pmap(log1p, 1:M, batch_size=ceil(Int,M/nworkers())) return sum(r) end @btime pmap_sum() #4.120 s (7012363 allocations: 181.55 MiB)

I don’t think it should matter for this case, but try adding a caching pool.

One thing I’ve been wondering about is the difference between mutithreading and parallel processing. Can someone explain to me when I should use the former when the latter, or point me to some? Up till now I’ve been just using threading because it seems like a simpler way of getting a performance in…

Multiprocessing, which we’re calling parallel here, is not shared memory. It can be distributed across multiple computers (nodes of an HPC). You don’t want to use a SharedArray unless you have to: you should limit the amount of data that is being shared and be careful about exchanging data. But it s…

Right, That was kind of what I was thinking too, thanks for the clarification! Basically whenever I can partition my data in such a way that is a standalone portion of the final problem, parallel processing would be a good idea, whereas if all parts of the divisible problem need to same/full set of …

[image] louisponet: Right, That was kind of what I was thinking too, thanks for the clarification! Basically whenever I can partition my data in such a way that is a standalone portion of the final problem, parallel processing would be a good idea, whereas if all parts of the divisible problem n…

[image] ChrisRackauckas: try adding a caching pool. If that’s what you mean, it doesn’t really help. function pmap_sum() M = 1000000 r = pmap(CachingPool(workers()),log1p, 1:M, batch_size=ceil(Int,M/nworkers())) return sum(r) end @btime pmap_sum() #4.379 s (7014529 allocations: 181.…

mohamed—I am changing the solution to your answer. if you see a “caching pool” improvement, please edit to add it to your previous answer. for now, can you please edit your earlier post to add the batch_size version to the plain one? regards, /iaw

there is something else I do not understand. why is mohamed’s simple_loop_sum() slower than parallel other versions, specifically sharedarray_parallel_sum(), even with one processor (nprocs()==1)?? (I also confirmed it on my own computer.)

I cannot observe what you are saying. These are the timings for 1 processor. julia> @btime simple_loop_sum(); 12.584 ms (2 allocations: 7.63 MiB) julia> @btime sharedarray_parallel_sum(); 14.997 ms (209 allocations: 10.00 KiB) julia> @btime pmap_sum(); 2.154 s (7999569 allocations: 152.59 M…

Simple Parallel Examples for Embarrassingly Simple Problems

Specific Domains Julia at Scale

ChrisRackauckas February 4, 2018, 8:14am 16

Kind of. It’s more like, if you’re on a single shared memory machine (i.e. one computer) you should probably use multithreading. Anything else needs multiprocessing. (Though there can be some extra complications)

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Simple Parallel Examples for Embarrassingly Simple Problems

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