Please take a look over here: GAL github repository

The README summarizes some of the main motivations of the library and some of the advantages I’m shooting for.

Perhaps a bit more regarding implementation detail, the library fully separates the data dependencies of an expression’s evaluation from the expression itself. This allows the expression to be reduced at compile time so that representations can remain sparse. Furthermore, this eliminates spurious operations that compilers cannot generally eliminate due to the nature of floating point arithmetic. The library supports a general (p, q, r) metric signature although most of my testing thus far has been in P(R^*_2) and P(R^*_3). Other features are that the computational backend can be swapped out which is something I intend on doing in the future to support generation of shader routines for GPU acceleration (or even HDL support, but SPIR-V is the target first). The library also supports field elements of arbitrary order, so for example, it supports automatic differentiation using the dual (order 2) unit but also other more exotic number systems. I haven’t gotten to it, but I intend on supporting constraints on the polynomial generators to accelerate the computation even more. Currently, polynomial reduction does not require division, but the monomials are totally ordered and amenable to standard computational algebraic techniques.

The library itself has been in development for ~1.5 weeks so please don’t expect too much, but I figured I’d post it here for early feedback/comments/suggestions. Time permitting, I intend on paring down my renderer at home to be suitable as a demo/viewing application. This would run natively in C++ via Vulkan most likely for platform compatibility.

Thanks all! Really digging the community here so far.