Eigen (C++ library): Difference between revisions

Revision as of 18:14, 10 February 2022

Eigen is a template header-only C++ linear algebra library. It is one of the fastest and most popular.

Reference
For optimal performance, I recommend using the following flags when compiling.

-march=native and -mtune=native if running only locally
- Otherwise, at a minimum
- -mfma Enable fused multiply add
- -mavx2 Enable avx2 vector instructions
-DEIGEN_NO_DEBUG Set preprocessor define for eigen optimizations
-fopenmp OpenMP parallel execution

You can use Eigen::Map to create an eigen view for your existing data.

Eigen comes with a few SVD implementations in its SVD Module.
If you only need low-rank approximations then you may be interested in randomized SVD.
This can be 10-20x faster when calculating low-rank approximations on large matrices.
Github Implementation
Finding structure with randomness paper
Facebook Blog post

@@ Line 8: / Line 8: @@
 For optimal performance, I recommend using the following flags when compiling.<br>
 ====GCC====
-*<code>-mfma</code> Enable fused multiply add
+*<code>-march=native</code> and <code>-mtune=native</code> if running only locally
-*<code>-mavx2</code> Enable avx2 vector instructions
+**Otherwise, at a minimum
+**<code>-mfma</code> Enable fused multiply add
+**<code>-mavx2</code> Enable avx2 vector instructions
 *<code>-DEIGEN_NO_DEBUG</code> Set preprocessor define for eigen optimizations
 *<code>-fopenmp</code> OpenMP parallel execution