I'm aware that evaluating a vectorized operation (say, an elementwise product of two arrays) will result in the allocation of a temporary array. I'm surprised, though, at just how much memory this seems to consume in practice -- unless there's something I'm not understanding. Here is an extreme example:
julia> a = rand(2); b = rand(2); julia> @time a .*= b; elapsed time: 0.505942281 seconds (11612212 bytes allocated) julia> @time a .*= b; elapsed time: 1.4177e-5 seconds (800 bytes allocated) julia> @time a .*= b; elapsed time: 2.5334e-5 seconds (800 bytes allocated) 800 bytes seems like a lot of overhead given that a and b are both only 16 bytes each. Of course, this overhead (whatever it is) becomes comparatively less significant as we move to larger arrays, but it's still sizeable: julia> a = rand(20); b = rand(20); julia> @time a.*= b; elapsed time: 1.4162e-5 seconds (944 bytes allocated) julia> @time a.*= b; elapsed time: 2.3754e-5 seconds (944 bytes allocated) Can someone explain what's going on here?
