Eigen (C++ library): Difference between revisions

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===Data to Eigen===
===Data to Eigen===
You can use <code>Eigen::Map</code> to create an eigen view for your existing data.<br>
You can use [https://eigen.tuxfamily.org/dox/classEigen_1_1Map.html <code>Eigen::Map</code>] to create an eigen view for your existing data.
<syntaxhighlight lang="cpp">
This works with aligned or unaligned data, row-order or column-order, and different strides.<br>
</syntaxhighlight>
See [https://eigen.tuxfamily.org/dox/group__TutorialMapClass.html Eigen: Interfacing with raw buffers] for an example.


==Math==
==Math==

Revision as of 20:06, 16 April 2024

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

Website

Usage

Compilation

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

GCC

  • -march=native and -mtune=native if running only locally or -march=skylake if distributing to (Core ix-5000+) cpus.
    • 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
  • -O3 to enable optimizations

Data to Eigen

You can use Eigen::Map to create an eigen view for your existing data. This works with aligned or unaligned data, row-order or column-order, and different strides.
See Eigen: Interfacing with raw buffers for an example.

Math

SVD

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