Threads memory allocations

General Usage
1

Hi. I’m really trying to understand parallel programming in Julia. Can someone please explain to me why using threads requires so many memory allocations?

using BenchmarkTools
using .Threads

function test_serial(u)
    for i in eachindex(u)
        u[i] = threadid()
    end
end

function test_threads(u)
    @threads for i in eachindex(u)
        u[i] = threadid()
    end
end

u = zeros(Int64, 1000)


julia> @benchmark test_serial($u)
BenchmarkTools.Trial:
  memory estimate:  0 bytes
  allocs estimate:  0
  --------------
  minimum time:     336.647 ns (0.00% GC)
  median time:      347.059 ns (0.00% GC)
  mean time:        373.924 ns (0.00% GC)
  maximum time:     922.167 ns (0.00% GC)
  --------------
  samples:          10000
  evals/sample:     221

julia> @benchmark test_threads($u)
BenchmarkTools.Trial:
  memory estimate:  2.72 KiB
  allocs estimate:  29
  --------------
  minimum time:     8.099 μs (0.00% GC)
  median time:      9.700 μs (0.00% GC)
  mean time:        10.360 μs (0.00% GC)
  maximum time:     136.400 μs (0.00% GC)
  --------------
  samples:          10000
  evals/sample:     1
1 Like
2

I am not sure if that’s “so many allocations”, but as far as I know, this is just the overhead of using the parallel threading library.

1 Like
3

The for loop allocates tasks and fetches the result, thus it essentially it reduces to:

julia> @benchmark threadid()
BenchmarkTools.Trial: 
  memory estimate:  0 bytes
  allocs estimate:  0
  --------------
  minimum time:     4.150 ns (0.00% GC)
  median time:      4.282 ns (0.00% GC)
  mean time:        4.453 ns (0.00% GC)
  maximum time:     37.888 ns (0.00% GC)
  --------------
  samples:          10000
  evals/sample:     1000

vs

@benchmark ((t = @async threadid()); fetch(t))
BenchmarkTools.Trial: 
  memory estimate:  704 bytes
  allocs estimate:  7
  --------------
  minimum time:     14.258 μs (0.00% GC)
  median time:      16.535 μs (0.00% GC)
  mean time:        18.000 μs (0.00% GC)
  maximum time:     97.452 μs (0.00% GC)
  --------------
  samples:          10000
  evals/sample:     1

times your threads for the memory estimate

2 Likes