it++ vs. Eigen - part 2 - performance results

Following the first part of this post, where I compared some properties of it++ vs. Eigen, two popular linear algebra packages (it++ is an interface to BLAS/LaPaCK).  I took some time for looking more closely at their performance.

I am using Graphlab's collaborative filtering library, where the following algorithms are implemented:
ALS (alternating least squares), Tensor ALS (Alternating least squared on 3D tensor), PMF (probablistic matrix factoriation) , BPTF (Bayesian prob. tensor factorization), SVD++, NMF (non-negative matrix factorization), Lanczos algorithm, Weighted ALS, ALS with sparse factor matrices.

Each algorithm was tested once on top of it++ and a second time on Eigen, using a subset of Netflix data described here.

Following is a summary of the results:
* Installation: Eigen has no installation, since it is composed of header files. it++ has a problematic installation on many systems, since installation packages are available on only part of the platforms, and potentially BLAS and LaPaCK should bre preinstalled.
* Licensing: Eigen has LGPL3+ license, while it++ has a limiting GPL license.
* Compilation: Eigen compilation tends to be slower because of excessive template usage.
* Performance: Typically Eigen is slightly faster as shown in the graphs below.
* Accuracy: Eigen is more accurate when using ldlt() method (relative to it++ solve() method), while it++ performs better on some other problems, when using backslash() least squares solution.

Here is timing performance plot. Lower is running time is better.


Here is accuracy plot. Lower training RMSE (root square mean error) is better.


Overall, the winner is: Eigen - because of simpler installation, equivalent if not better performance and a more flexible license.