Currently, I have defined separate classes for each data type where the number of bits for each field can be customized. I understand the trimming existing data types like float64 is a solution, but I’d rather avoid that because a user may choose a bit width for exponent that is larger than what IEEE standard supports.
Can you please elaborate your last sentence a little bit? Or a snippet of code that I can run using my data types?
If you mean something like doing matrix multiplication using NumPy and on my data types, I don’t think the current implementation works. Because my NumPy is complied to use openblas, but matrix multiplication is performed using a single core, so I assume NumPy is using its own engine rather than openblas.
I think my implementation is too simple at the moment because it simply defines basic operations such as __add__, __sub__, etc. on the data types. I’m not familiar with BLAS, but I assume there’s more I have to do to make use of openblas, MKL, etc.