Mauricio,

Is the time difference that you observed material? i.e., is that an important difference for your use case?

Frank

On Thu, Feb 9, 2017 at 11:07 PM, Nandish Jayaram <njayaram@pivotal.io> wrote:
Hi Mauricio,

I briefly looked through the code and it seems like the dot product in cosine_similarity is based on what is there in the Eigen library. 
The dot product in array_dot seems to be using a native implementation of the same. Apparently, dot product in Eigen is faster than
the native implementation. Looks like it might be a good idea to move array_dot also to Eigen based dot product!

NJ

On Thu, Feb 9, 2017 at 10:19 AM, Mauricio Scheffer <mauricioscheffer@gmail.com> wrote:
Hi,

I just started evaluating MADlib and one of the first things I tried is how it performs for dot product and cosine similarity.

So first I set up some test data (1000000 rows of 150-element float8[])
Then I ran array_dot and cosine_similarity on it:

select * from (
  select cosine_similarity -- or array_dot
    (a_vector, (select array_agg(random()::float8) from generate_series(0, 150))) c
    from vectors
) x
order by c desc
limit 10

On my machine, cosine_similarity takes 1.3s while array_dot takes 3s, which is rather unexpected... I would have expected a dot product to be much faster than calculating cosine similarity.
Can anyone shed some light on this?

Thanks,
Mauricio