OhMyThreads

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Simple Multithreading in Julia

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OhMyThreads.jl is meant to be a simple, unambitious package that provides user-friendly ways of doing task-based multithreaded calculations in Julia. Most importantly, with a focus on data parallelism, it provides an API of higher-order functions (e.g. tmapreduce) as well as a macro API @tasks for ... end (conceptually similar to @threads).

Example

using OhMyThreads: tmapreduce, @tasks
using BenchmarkTools: @btime
using Base.Threads: nthreads

# Variant 1: function API
function mc_parallel(N; ntasks=nthreads())
    M = tmapreduce(+, 1:N; ntasks) do i
        rand()^2 + rand()^2 < 1.0
    end
    pi = 4 * M / N
    return pi
end

# Variant 2: macro API
function mc_parallel_macro(N; ntasks=nthreads())
    M = @tasks for i in 1:N
        @set begin
            reducer=+
            ntasks=ntasks
        end
        rand()^2 + rand()^2 < 1.0
    end
    pi = 4 * M / N
    return pi
end

N = 100_000_000
mc_parallel(N) # gives, e.g., 3.14159924

@btime mc_parallel($N; ntasks=1) # use a single task (and hence a single thread)
@btime mc_parallel($N)           # using all threads
@btime mc_parallel_macro($N)     # using all threads

With 5 threads, timings might be something like this:

417.282 ms (14 allocations: 912 bytes)
83.578 ms (38 allocations: 3.08 KiB)
83.573 ms (38 allocations: 3.08 KiB)

(Check out the full Parallel Monte Carlo example if you like.)

Documentation

For more information, please check out the documentation of the latest release (or the development version if you're curious).