Julia, similarly to most scientific languages, has a very convenient syntax for array slicing. Consider the following example that sums each column of a two-dimensional matrix. First, we will define a function that sums the elements of a vector to produce a scalar. We will then use this function inside a loop to sum the columns of a matrix, passing each column one by one to our vector adder, as follows:
function sum_vector(x::Array{Float64, 1})
s = 0.0
for i = 1:length(x)
s = s + x[i]
end
return s
end
function sum_cols_matrix(x::Array{Float64, 2})
num_cols = size(x, 2)
s = zeros(num_cols)
for i = 1:num_cols
s[i] = sum_vector(x[:, i])
end
return s
endThe x[:, j] syntax denotes all the row elements of the jth column. In other words, it slices a matrix into its individual columns. Benchmarking this function, we will notice that the allocations and GC times are quite high. Take a look:
julia> @benchmark sum_cols_matrix(rand(1000, 1000)) ===...