如何加速这个已经矢量化的 matlab 代码

Question

I'm trying to speed up steps 1-4 in the following code (the rest is setup that will be predetermined for my actual problem.)

% Given sizes:
m = 200;
n = 1e8;

% Given vectors:
value_vector = rand(m, 1);
index_vector = randi([0 200], n, 1);

% Objective: Determine the values for the values_grid based on indices provided by index_grid, which
%            correspond to the indices of the value in value_vector

% 0. Preallocate
values = zeros(n, 1);

% 1. Remove "0" indices since these won't have values assigned
nonzero_inds = (index_vector ~= 0);

% 2. Examine only nonzero indices
value_inds = index_vector(nonzero_inds);

% 3. Get the values for these indices
nonzero_values = value_vector(value_inds);

% 4. Assign values  to output (0 for those with 0 index)
values(nonzero_inds) = nonzero_values;

Here's my analysis of these portions of the code:

1. Necessary since the index_vector will contain zeros which need to be ferreted out. O(n) since it's just a matter of going through the vector one element at a time and checking (value ∨ 0)
2. Should be O(n) to go through index_vector and retain those that are nonzero from the previous step
3. Should be O(n) since we have to check each nonzero index_vector element, and for each element we access the value_vector which is O(1).
4. Should be O(n) to go through each element of nonzero_inds , access corresponding values index, access the corresponding nonzero_values element, and assign it to the values vector.

The code above takes about 5 seconds to run through steps 1-4 on 4 cores, 3.8GHz. Do you all have any ideas on how this could be sped up? Thanks.

Answer 1

Wow, I found something really interesting. I saw this link in the "related" section about indexing vectors being inefficient in Matlab sometimes, so I decided to try a for loop. This code ended up being an order of magnitude faster!

for i = 1:n
    if index_vector(i) > 0
        values(i) = value_vector(index_vector(i));
    end
end

EDIT: Another interesting thing, unfortunately detrimental to my problem though. The speed of this solution depends on the amount of zeros in the index_vector. With index_vector = randi([0 200]); , a small proportion of the values are zeros, but if I try index_vector = randi([0 1]) , approximately half of the values will be zero and then the above for loop is actually an order of magnitude slower. However, using ~= instead of > speeds the loop back up so that it's on a similar order of magnitude. Very interesting and odd behavior.

Answer 2

if you stick to matlab and the flow of the algorithm you want , and not doing this in fortran or c, here's a small start:

change the randi to rand, and round by casting to uint8 and use the > logical operation that for some reason is faster at my end

to sum up:

value_vector = rand(m, 1 );
index_vector = uint8(-0.5+201*rand(n,1) );
values = zeros(n, 1);
values=value_vector(index_vector(index_vector>0));

this improved at my end by a factor 1.6