What is the recommended way to iterate a matrix over rows?

As of Julia 1.1, there are iterator utilities for iterating over the columns or rows of a matrix. To iterate over rows:

M = [1 2 3; 4 5 6; 7 8 9]

for row in eachrow(af)
    println(row)
end

Will output:

[1, 2, 3]
[4, 5, 6]
[7, 8, 9]

According to my experiences, explicit iterations are much faster than comprehensions.

And iterating over columns are also a good advice.

Besides, you can use the new macros @simd and @inbounds to further accelerate it.

The solution you listed yourself, as well as mapslices, both work fine. But if by "recommended" what you really mean is "high-performance", then the best answer is: don't iterate over rows.

The problem is that since arrays are stored in column-major order, for anything other than a small matrix you'll end up with a poor cache hit ratio if you traverse the array in row-major order.

As pointed out in an excellent blog post, if you want to sum over rows, your best bet is to do something like this:

msum = zeros(eltype(m), size(m, 1))
for j = 1:size(m,2)
    for i = 1:size(m,1)
        msum[i] += m[i,j]
    end
end

We traverse both m and msum in their native storage order, so each time we load a cache line we use all the values, yielding a cache hit ratio of 1. You might naively think it's better to traverse it in row-major order and accumulate the result to a tmp variable, but on any modern machine the cache miss is much more expensive than the msum[i] lookup.

Many of Julia's internal algorithms that take a dims keyword, like sum(m; dims=2), handle this for you.