3 ms·
One difference is that their approach doesn't scale to 64-bit floats AFAIK. My code and their approach are actually quite different, with different goals, diffe
by nsajko 3y ago
One difference is that their approach doesn't scale to 64-bit floats AFAIK. My code and their approach are actually quite different, with different goals, different places where they're smart and different places where they could be improved.
I hope to improve my code based on their ideas when I finally read their papers, however I still have plenty of my own that I want to implement.
See also this thread from twenty days ago, someone asked me basically the same thing, linking an earlier paper by Lim and Nagarakatte (et al):
https://news.ycombinator.com/item?id=36201197 https://news.ycombinator.com/item?id=36201197
- lifthrasiir 3y agoCool! I think the RLIBM approach is indeed fresh and has potentials, but I was never sure if it can be generalized to domains past binary32 when we are looking for correctly rounded results. I guess it's a fundamental difference between RLIBM and your algorithm---you don't guarantee correctly rounded results, right?
- nsajko 3y ago> I guess it's a fundamental difference between RLIBM and your algorithm---you don't guarantee correctly rounded results, right? Yes, but note that the linked package doesn't even attempt to implement a polynomial approximation (for use in software), rather it just finds the coefficients of a polynomial. Another WIP package of mine will use FindMinimaxPolynomial to actually implement the approximation (with source code output etc), however even that package was never meant to guarantee correctly rounded results. In particular their work is more ambitious than mine in that they seem to account for the range reduction and output compensation while looking for the coefficients with LP, which enables them to guarantee correctly rounded results.