以下函数nested_arrays（令人惊讶地）生成一个“深度”n的嵌套数组。但是，在使用n（2，3等小值运行时，运行并显示输出需要相当长的时间。
julia> nested_arrays(n) = n == 1 ? [1] : [nested_arrays(n - 1)]
nested_arrays (generic function with 1 method)
julia> nested_arrays(1)
1-element Array{Int64,1}:
1
julia> nested_arrays(2)
1-element Array{Array{Int64,1},1}:
[1]
julia> nested_arrays(3)
1-element Array{Array{Array{Int64,1},1},1}:
Array{Int64,1}[[1]]
julia> nested_arrays(10)
1-element Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Int64,1},1},1},1},1},1},1},1},1},1}:
Array{Array{Array{Array{Array{Array{Array{Array{Int64,1},1},1},1},1},1},1},1}[Array{Array{Array{Array{Array{Array{Array{Int64,1},1},1},1},1},1},1}[Array{Array{Array{Array{Array{Array{Int64,1},1},1},1},1},1}[Array{Array{Array{Array{Array{Int64,1},1},1},1},1}[Array{Array{Array{Array{Int64,1},1},1},1}[Array{Array{Array{Int64,1},1},1}[Array{Array{Int64,1},1}[Array{Int64,1}[[1]]]]]]]]]
有趣的是，当在行尾使用@time宏或;时，结果的计算时间相对较少。相反，REPL中结果的实际显示占用了大部分时间。
这种奇怪的行为没有在例如Python中显示。
In [1]: def nested_lists(n):
...: if n == 1:
...: return [1]
...: return [nested_lists(n - 1)]
...:
In [2]: nested_lists(10)
Out[2]: [[[[[[[[[[1]]]]]]]]]]
In [3]: %time nested_lists(100)
CPU times: user 0 ns, sys: 0 ns, total: 0 ns
Wall time: 37.7 µs
Out[3]: [[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[[1]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
为什么朱莉娅这个功能这么慢？ Julia是否重新编译display中不同类型T的{{1}}函数？如果是这样，为什么会这样？
可以提高此代码的速度，还是应该在Julia中完成？实际上我对此的主要关注点是，例如，加载一个复杂的嵌套JSON文件，其中只使用Array{T, 1} - 维数组是不可能的。
答案 0 :(得分：5)
是的，这完全是由于编译时间。您可以通过@time display来查看此内容。第二次显示它很快：
julia> nested_arrays(n) = n == 1 ? [1] : [nested_arrays(n - 1)]
nested_arrays (generic function with 1 method)
julia> @time display(nested_arrays(15));
1-element Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Int64,1},1},1},1},1},1},1},1},1},1},1},1},1},1},1}:
Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Int64,1},1},1},1},1},1},1},1},1},1},1},1},1}[Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Int64,1},1},1},1},1},1},1},1},1},1},1},1}[Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Int64,1},1},1},1},1},1},1},1},1},1},1}[Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Int64,1},1},1},1},1},1},1},1},1},1}[Array{Array{Array{Array{Array{Array{Array{Array{Array{Int64,1},1},1},1},1},1},1},1},1}[Array{Array{Array{Array{Array{Array{Array{Array{Int64,1},1},1},1},1},1},1},1}[Array{Array{Array{Array{Array{Array{Array{Int64,1},1},1},1},1},1},1}[Array{Array{Array{Array{Array{Array{Int64,1},1},1},1},1},1}[Array{Array{Array{Array{Array{Int64,1},1},1},1},1}[Array{Array{Array{Array{Int64,1},1},1},1}[Array{Array{Array{Int64,1},1},1}[Array{Array{Int64,1},1}[Array{Int64,1}[[1]]]]]]]]]]]]]]
11.682721 seconds (8.83 M allocations: 371.698 MB, 1.82% gc time)
julia> @time display(nested_arrays(15));
1-element Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Int64,1},1},1},1},1},1},1},1},1},1},1},1},1},1},1}:
Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Int64,1},1},1},1},1},1},1},1},1},1},1},1},1}[Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Int64,1},1},1},1},1},1},1},1},1},1},1},1}[Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Int64,1},1},1},1},1},1},1},1},1},1},1}[Array{Array{Array{Array{Array{Array{Array{Array{Array{Array{Int64,1},1},1},1},1},1},1},1},1},1}[Array{Array{Array{Array{Array{Array{Array{Array{Array{Int64,1},1},1},1},1},1},1},1},1}[Array{Array{Array{Array{Array{Array{Array{Array{Int64,1},1},1},1},1},1},1},1}[Array{Array{Array{Array{Array{Array{Array{Int64,1},1},1},1},1},1},1}[Array{Array{Array{Array{Array{Array{Int64,1},1},1},1},1},1}[Array{Array{Array{Array{Array{Int64,1},1},1},1},1}[Array{Array{Array{Array{Int64,1},1},1},1}[Array{Array{Array{Int64,1},1},1}[Array{Array{Int64,1},1}[Array{Int64,1}[[1]]]]]]]]]]]]]]
0.001688 seconds (2.38 k allocations: 102.766 KB)
那为什么这么慢？这里的显示递归遍历所有数组，并将它们嵌套在彼此内部。这是递归调用show有14种不同的类型 - 一个有14个嵌套数组，然后是13个嵌套数组的元素，然后是12个元素......依此类推！每个show方法都可以独立编译。为特定元素类型编译专用方法是Julia如何生成非常高效代码的关键部分。这意味着它能够专门化每个元素上完成的每个操作，而无需任何运行时类型检查或调度。不幸的是，在这种情况下，它会妨碍它。
您可以使用Any[]数组解决此问题;在JSON文件的上下文中，这非常有意义，因为您不知道它是否包含字符串或数组或数字等。这要快得多，因为它只需要编译show方法对于一个Any[]数组，然后递归使用它。
# new session
julia> nested_arrays(n) = n == 1 ? Any[1] : Any[nested_arrays(n - 1)]
nested_arrays (generic function with 1 method)
julia> @time display(nested_arrays(15));
1-element Array{Any,1}:
Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[1]]]]]]]]]]]]]]
1.571632 seconds (767.12 k allocations: 32.472 MB, 1.04% gc time)
julia> @time display(nested_arrays(15));
1-element Array{Any,1}:
Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[1]]]]]]]]]]]]]]
0.000606 seconds (839 allocations: 30.859 KB)
julia> @time display(nested_arrays(100));
1-element Array{Any,1}:
Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[Any[1]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]]
0.002523 seconds (17.76 k allocations: 579.297 KB)