subject:"Spark SVD benchmark for dense matrices"

Re: Spark SVD benchmark for dense matrices

2017-08-10 Thread Anastasios Zouzias

Hi Jose, Just to note that in the databricks blog they state that they compute the top-5 singular vectors, not all singular values/vectors. Computing all is much more computational intense. Cheers, Anastasios Am 09.08.2017 15:19 schrieb "Jose Francisco Saray Villamizar" < jsa...@gmail.com>:

Spark SVD benchmark for dense matrices

2017-08-09 Thread Jose Francisco Saray Villamizar

Hi everyone, I am trying to invert a 5000 x 5000 Dense Matrix (99% non-zeros), by using SVD with an approach simmilar to : https://stackoverflow.com/questions/29969521/how-to-compute-the-inverse-of-a-rowmatrix-in-apache-spark The time Im getting with SVD is close to 10 minutes what is very long