on
driver's memory, and thats why number of columns of A cannot be be very
large. (10k x 10k matrix will close to 1 GB). Once you've R, a local SVD
implementation will be needed to to compute SVD of R. Now, this is not a
very general method, but I think this is good enough for most of the cases.
imran
t;> https://github.com/apache/systemml/pull/273/files#diff-488f0
> >> > >> 6e290f7a54db2e125f7bc608971R27
> >> > >> ).
> >> > >> The idea there was to build up a distributed SVD using invocations
> of
> >> > svd
> >> > >> on your lo
nd run the spark primitives needed.
>>
>> Cons:
>> - Implementing SVD, whether in DML or C, is a fair amount of work
>> - There would not be a straightforward call to the svd gpu library. In
>> fact, each of the linear algebra primitives would be accelerated on the
>
:
Pros:
- Use of GPU libraries (cuSolver) and CPU libraries (Apache Commons Math)
can be made, these are already optimized (in case of the GPU)
- If a better SVD implementation is available via a library, that can
easily be plugged in.
Cons:
- Would have to come up with an algorithm to imple
