Performance for loops in Julia

Question

I tried the following codes, but the performance is very different among the codes. I heard that codes at the top level are not suitable for numerical calculations, but performance also seems to depend on whether the top-level variables (here, N) appear in the for-loops range. Is it always best to avoid such top-level variables?

N = 10000000

# case1 (slow)
x = 0.0
@time for k = 1:N
    x += float( k )
end

# case2 (slow)
@time let
    y = 0.0
    for j = 1:N
        y += float( j )
    end
end

# case3 (very fast)
@time let
    n::Int64
    n = N
    z = 0.0
    for m = 1:n
        z += float( m )
    end
end

# case 4 (slow)
function func1()
  c = 0.0
  for i = 1:N                                             
    c += float( i )
  end
end

# case 5 (fast)
function func2( n )                                          
  c = 0.0
  for i = 1:n
    c += float( i )
  end
end

# case 6 (fast)
function func3()
  n::Int
  n = N
  c = 0.0
  for i = 1:n                                   
    c += float( i )
  end
end

# case 7 (slow)
function func4()
  n = N        # n = int( N ) is also slow                                  
  c = 0.0
  for i = 1:n
    c += float( i )
  end
end

@time func1()
@time func2( N )
@time func3()
@time func4()

The result obtained using Julia 0.3.7 (on Linux x86_64),

elapsed time: 2.595440598 seconds (959985496 bytes allocated, 10.70% gc time)
elapsed time: 2.469471127 seconds (959983688 bytes allocated, 11.49% gc time)
elapsed time: 1.608e-6 seconds (16 bytes allocated)
elapsed time: 2.535243279 seconds (960021976 bytes allocated, 11.21% gc time)
elapsed time: 0.002601149 seconds (75592 bytes allocated)
elapsed time: 0.003471583 seconds (84456 bytes allocated)
elapsed time: 2.480343146 seconds (960020752 bytes allocated, 11.48% gc time)

Answer 1

: " ?" , , ", ", ,

const N = 10000000

2 , 3.