Question on semantics of loops, maps, and broadcast

I’ve been playing with writing things in different forms, for example the following:

function f_by_loop!(data,x)
    @inbounds for i in eachindex(data)
        data[i] = min(x,data[i])
    end
end

vs data .= min.(x,data) or map!(y -> min(y,x), data, data).
where, for example, data=randn(100_000_000) and x=1.0.

These do the same thing and once I added @inbounds to the loop, all three performed similarly.

The broadcast form is definitely the most concise and I’m guessing the least error prone.

My question is: Is broadcast also preferable to the manual loop because it does not unnecessarily specify an order of operation, making it easier for the compiler to optimize?

1 Like

In my experience, the advantages of broadcasting are:

  1. Loop fusion (https://julialang.org/blog/2017/01/moredots), which can also be achieved with manual loops, just more verbosely.
  2. Support for efficient operations on more esoteric containers. For example, broadcasting over a sparse array can avoid unnecessarily traversing all of the zero entries:
julia> function f_loop!(y, x)
         for i in eachindex(y)
           @inbounds y[i] = x[i]
         end
       end
f_loop! (generic function with 1 method)

julia> x = sprand(100, 100, 0.001);

julia> @btime y .= $x setup=(y = similar(x));
  37.082 ns (0 allocations: 0 bytes)

julia> @btime f_loop!(y, x) setup=(y = similar(x))
  60.394 μs (0 allocations: 0 bytes)

There’s an efficient way to iterate over only the nonzeros in a sparse array, but broadcasting is smart enough to do that for you.

This also enables clever packages like https://github.com/tkoolen/TypeSortedCollections.jl which allows broadcasting across heterogeneous containers.

2 Likes

I’m not entirely sure what you mean by “preferable” (some options: more performant, considered better style, etc.), but with loops please do note that the @simd annotation can be used to indicate to the compiler that the loop iterations are independent and can be reordered.

2 Likes

Note also that, even if they overlap almost exactly for functions of a single argument, they can mean different things for functions of two or more arguments.

Compare

map((x,y) -> x + y, ones(1, 4), ones(4))

and

broadcast((x,y) -> x + y, ones(1, 4), ones(4))
3 Likes

Good point about the dimensional magic of broadcast.

Thanks,

I figured out that the loop version was using SIMD instructions once I applied @inbounds. I believe @simd wasn’t necessary because there is no reduction aspect of the problem I chose as an example.

Thanks for the link - that was helpful.