Simple Multithreading in Julia
| Documentation | Build Status | Quality |
|---|---|---|
  |
  |
  |
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).
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 threadsWith 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.)
For more information, please check out the documentation of the latest release (or the development version if you're curious).