The issue was bounds check elimination, which is already a problem for 1d arrays. Currently it's very hard to eliminate them because arrays can get resized out from under you at any point.
> On Dec 28, 2013, at 10:08 AM, Tim Holy <[email protected]> wrote: > > Holding columns in separate entries is a great way. However, if you need to > do > linear algebra on the matrix at intermediate stages during its growth, then > you'll have a lot of needless copying occurring while you convert the column- > storage into a matrix. > > In such circumstances, there's a sneaky workaround: > > reshape1(a::Vector, dims::Dims) = pointer_to_array(pointer(a), dims) > > a = zeros(3) > c = ones(3) > append!(a, c) > A = reshape1(a, (3, div(length(a),3))) > c += 1 > append!(a, c) > A = reshape1(a, (3, div(length(a),3))) > > Using pointer_to_array circumvents the ordinary protections built into > resize! > There's still allocation occurring (it has to build a new Array "wrapper" on > each iteration), but it avoids copying any data, and for large amounts of > data > this is a big win. > > Even better would be to generalize resize! to support the final dimension of > any array. I seem to remember Stefan had a reason why this might be > problematic, but I confess I forget what it is. > > --Tim > > >> On Friday, December 27, 2013 05:45:15 PM Sheehan Olver wrote: >> What's the "best" way of constructing an array that can grow adaptively? >> For example, it has fixed m rows but the number of columns grows as an >> algorithm proceeds. Unfortunately, >> >> resize! >> >> doesn't work for 2d arrays. It does work for Array{Array{Float64,1},1}, >> but not sure that's optimal.
