This paper presents a novel method for generating a single polynomial approximation that produces correctly rounded results for all inputs of an elementary function for multiple representations. The generated polynomial approximation has the nice property that the first few lower degree terms produce correctly rounded results for specific representations of smaller bitwidths, which we call progressive performance. To generate such progressive polynomial approximations, we approximate the correctly rounded result and formulate the computation of correctly rounded polynomial approximations as a linear program inspired by our RLibm project. In contrast to our prior work, we avoid storing large lookup tables for the polynomial coefficients. We observe that the problem of computing polynomial approximations to elementary functions is a linear programming problem in low dimensions, i.e., with a small number of unknowns. We design a fast randomized algorithm for efficiently computing polynomial approximations with progressive performance. Our method produces polynomial approximations that are faster than the RLibm project and other mainstream libraries while also having progressive performance.
翻译:本文展示了生成单一多元近似的新颖方法, 该方法为多个表达式基本函数的所有输入生成正确四舍五入的结果。 生成的多元近近似具有一个良好的属性, 即前几个低度术语为较小位宽的具体表达方式产生正确四舍五入的结果, 我们称之为渐进性性能。 为了生成这种渐进性多位近近近近, 我们比较了正确四舍五入的结果, 并将正确四舍五入的多元近近近近作为受我们的 RLibm 项目启发的线性程序进行计算。 与我们先前的工作相比, 我们避免存储多位元系数的大型查看表。 我们观察到, 将多位近近似值计算为基本函数的问题是一个低维的线性编程问题, 也就是数量不多的未知。 我们设计了一个快速随机算法, 高效计算多位近似和渐进性能。 我们的方法生成的多位近似值比 RLibm 项目和其他主流图书馆更快, 同时还有渐进性能 。