Using it for years, and mostly happy with that library. The performance is awesome for very long vectors / large matrices.
It’s less than ideal for small things when the size is known at compile-time. If one knows SIMD intrinsics, in some of these cases the Eigen’s implementation can be outperformed by a large factor like 2-4. Also it’s very hard to mess with RAM layout of some things (like sparse matrices), just too many layers of abstraction and too much template metaprogramming.
But still, out of the box the usability of Eigen is awesome. And until the code is written, debugged, integrated and benchmarked, it’s generally impossible to tell whether a particular algorithm gonna be a performance bottleneck. That’s why I’m mostly happy with the library.
Can confirm the poor performance on small matrices (less than 20x20). This is a problem particularly for robotics applications when your entities are positions, velocities, etc.
https://stackoverflow.com/questions/58071344/is-eigen-slow-a...
Huh, interesting - this is news to me, as I use Eigen all the time/see it used all over for robotics. Is there a good replacement for robotics-specific operations/small matrices generally (I see some people mentioning DirectXMath?)? Or is the tradeoff just between spending the time and effort to write SIMD intrinsics yourself vs. lower performance but greater convenience with Eigen?
One advantage of Eigen's approach that I haven't seen mentioned here is that its templated design makes it easy to substitute custom scalar types for operations, which helps enable straightforward automatic differentiation and other such tools (e.g. I'm currently using Eigen to make a tracing JIT for computations like FK, etc. over scenegraphs).