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Asked: 2026-05-19T09:23:41+00:00

I’ve been wanting to write my own multithreaded realtime raytracer in C++, but I

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I’ve been wanting to write my own multithreaded realtime raytracer in C++, but I don’t want to implement all the vector and matrix logic that comes with it. I figured I’d do some research to find a good library for this, but I haven’t had much success…

It’s important that the implementation is fast, and preferably that it comes with some friendly licensing. I’ve read that boost has basic algebra, but I couldn’t find anything on how good it was regarding its speed.

For the rest, Google gave me Armadillo, which claims to be very fast, and compares itself to certain other libraries that I haven’t heard of.
Then I got Seldon, which also claims to be efficient and convenient, although I couldn’t find out where exactly they are on the scale.
Lastly I read about Eigen, which I’ve also seen mentioned here on StackOverflow while searching here.

In the CG lecture at my university, they use HLSL for the algebra (making the students implement/optimise parts of the raytracer), which got me thinking whether or not I could use GLSL for this. Again, I have no idea what option is most efficient, or what the general consensus is on algebra libraries. I was hoping SO could help me out here, so I can get started with some real development 🙂

PS: I tried linking to sites, but I don’t have enough rep yet

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1 Answer

  1. Editorial Team

    I’d recommend writing your own routines. When I wrote my raytracer, I found that most of the algebra used the same small collection of methods. Basically all you need is a vector class that supports addition, subtraction, etc. And from there all you really need is Dot and Cross.

    And to be honest using GLSL isn’t going to give you much more than that anyways (they only support dot, cross and simple vector math, everything else must be hand coded). I’d also recommend prototyping in C++ then moving to CUDA afterwards. It’s rather difficult to debug a GPU code, so you can get it working in the CPU then recode it a bit to work in CUDA.

    In reality raytracers are fairly simple. It’s making them fast that is hard. It’s the acceleration structures that are going to take most of your time and optimization. At least it did for me.

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