Error in Ulps of the Multiplication or Division by a Correctly-Rounded Function or Constant in Binary Floating-Point Arithmetic

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS IEEE Transactions on Emerging Topics in Computing Pub Date : 2023-07-18 DOI:10.1109/TETC.2023.3294986
Nicolas Brisebarre;Jean-Michel Muller;Joris Picot
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Abstract

Assume we use a binary floating-point arithmetic and that $\operatorname{RN}$ is the round-to-nearest function. Also assume that $c$ is a constant or a real function of one or more variables, and that we have at our disposal a correctly rounded implementation of $c$ , say $\hat{c}= \operatorname{RN}(c)$ . For evaluating $x \cdot c$ (resp. $ x / c$ or $c / x$ ), the natural way is to replace it by $\operatorname{RN}(x \cdot \hat{c})$ (resp. $ \operatorname{RN}(x / \hat{c})$ or $\operatorname{RN}(\hat{c}/ x)$ ), that is, to call function $\hat{c}$ and to perform a floating-point multiplication or division. This can be generalized to the approximation of $n/d$ by $\operatorname{RN}(\hat{n}/\hat{d})$ and the approximation of $n \cdot d$ by $\operatorname{RN}(\hat{n} \cdot \hat{d})$ , where $\hat{n} = \operatorname{RN}(n)$ and $\hat{d} = \operatorname{RN}(d)$ , and $n$ and $d$ are functions for which we have at our disposal a correctly rounded implementation. We discuss tight error bounds in ulps of such approximations. From our results, one immediately obtains tight error bounds for calculations such as $\mathtt {x * pi}$ , $\mathtt {ln(2)/x}$ , $\mathtt {x/(y+z)}$ , $\mathtt {(x+y)*z}$ , $\mathtt {x/sqrt(y)}$ , $\mathtt {sqrt(x)/{y}}$ , $\mathtt {(x+y)(z+t)}$ , $\mathtt {(x+y)/(z+t)}$ , $\mathtt {(x+y)/(zt)}$ , etc. in floating-point arithmetic.
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二进制浮点运算中被正确取整的函数或常数乘除的 Ulps 误差
假设我们使用二进制浮点运算,$\operatorname{RN}$ 是四舍五入函数。还假设 $c$ 是一个常数或一个或多个变量的实函数,并且我们有一个正确舍入的 $c$ 实现,例如 $\hat{c}= \operatorname{RN}(c)$.对于计算 $x \cdot c$(即 $ x / c$ 或 $c / x$),自然的方法是用 $\operatorname{RN}(x \cdot \hat{c})$ 替换它(即 $ \operatorname{RN}(x \cdot \hat{c})$ 替换它)。$ \operatorname{RN}(x / \hat{c})$ 或 $\operatorname{RN}(\hat{c}/ x)$),也就是说,调用函数 $\hat{c}$ 并执行浮点乘法或除法。这可以推广到用 $\operatorname{RN}(\hat{n}/\hat{d})$ 近似 $n/d$ 和用 $\operatorname{RN}(\hat{n} \cdot \hat{d})$ 近似 $n \cdot d$、其中,$\hat{n} = \operatorname{RN}(n)$ 和 $\hat{d} = \operatorname{RN}(d)$ ,而 $n$ 和 $d$ 是我们可以正确舍入实现的函数。我们将讨论这种近似的 ulps 紧误差边界。从我们的结果中,我们可以立即得到诸如 $\mathtt {x * pi}$, $\mathtt {ln(2)/x}$, $\mathtt {x/(y+z)}$ 等计算的严格误差边界、$\mathtt {(x+y)*z}$, $\mathtt {x/sqrt(y)}$, $\mathtt {sqrt(x)/{y}}$, $\mathtt {(x+y)(z+t)}$, $\mathtt {(x+y)/(z+t)}$, $\mathtt {(x+y)/(zt)}$, 等等。在浮点运算中。
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来源期刊
IEEE Transactions on Emerging Topics in Computing
IEEE Transactions on Emerging Topics in Computing Computer Science-Computer Science (miscellaneous)
CiteScore
12.10
自引率
5.10%
发文量
113
期刊介绍: IEEE Transactions on Emerging Topics in Computing publishes papers on emerging aspects of computer science, computing technology, and computing applications not currently covered by other IEEE Computer Society Transactions. Some examples of emerging topics in computing include: IT for Green, Synthetic and organic computing structures and systems, Advanced analytics, Social/occupational computing, Location-based/client computer systems, Morphic computer design, Electronic game systems, & Health-care IT.
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Table of Contents Front Cover IEEE Transactions on Emerging Topics in Computing Information for Authors Special Section on Emerging Social Computing DALTON - Deep Local Learning in SNNs via local Weights and Surrogate-Derivative Transfer
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