Floating point arithmetic is rarely exact.
浮点运算很少是精确的。
Real Programmers scorn floating point arithmetic.
真正的程序员鄙视浮点运算。
The floating point arithmetic is very important for high performance arithmetic.
浮点运算是高性能计算研究中的一个重要领域。
Using block-floating point arithmetic, the processor can provide a high quality.
数据采用块浮点表示，提高了运算精度。
Floating point arithmetic is one of the most important one in high performance arithmetic.
浮点运算是高性能计算研究中的一个重要领域。
In this paper we mainly study for the key technology of super precision floating point arithmetic.
本文主要研究超高精度浮点运算中的关键技术。
While nearly every processor and programming language supports floating point arithmetic, most programmers pay little attention to it.
虽然几乎每种处理器和编程语言都支持浮点运算，但大多数程序员很少注意它。
It is best to reserve the use of floating point arithmetic for calculations that involve fundamentally inexact values, such as measurements.
最好将浮点运算保留用作计算本来就不精确的数值，譬如测量。
For reasons that concern the implementation of floating point arithmetic, we decided to train our net with these twenty counts divided by a normalizing factor.
由于关注浮点运算的执行，我们打算用一种规格化因素将这20字符统计分开来，并以此培训我们的网络。
Meanwhile, the single board computer itself has developed and found new applications be cause of its ability to make high precision operations of floating point arithmetic.
同时，单板机本身也因具有进行高精度浮点算术运算的能力而得到了新的开发应用。
A processor interprets a stream of data as instructions to execute; it has one or more processing units that perform integer and floating-point arithmetic as well as more advanced computations.
处理器将数据流解释为要执行的指令，它拥有一个或多个处理单元，用于执行整数和浮点运算以及更高级的计算。
SIGFPE - Arithmetic exception, integer divided by 0, or floating-point exception.
SIGFPE—算术异常、整数被零除或浮点异常。
Even under version 7.2, Vim does only floating-point arithmetic if one of the operands is explicitly floating-point.
即使对于版本7.2，如果其中一个运算对象被明确声明为浮点类型，那么Vim只支持浮点算术。
Remember, integer arithmetic is much faster than floating-point arithmetic, as it can usually be done directly by the processor, rather than relying on external FPUs or floating point math libraries.
记住，整形数运算要比浮点数运算快得多，因为处理器可以直接进行整型数运算，浮点数运算需要依赖于外部的浮点数处理器或者浮点数数学库。
The floating point environment functions are not always supported, and some platforms will not have support for IEEE arithmetic.
浮点环境函数并不是总被支持，有一些平台不会支持IEEE运算。
Note that, in this example, sum must be initialized to an explicit floating-point value; otherwise, all the subsequent computations will be done using integer arithmetic.
注意，在本例中，sum必须被初始化为一个显式的浮点值；否则，所有后续计算都将使用整数运算计算。
Many of the familiar algebraic rules for real number arithmetic do not always hold for floating-point arithmetic.
对于浮点算法而言，许多惯用的实数算法代数规则有时并不适用。
The reason beyond this is that those fast algorithms do reduce the complexity of computing but still one needs to use floating-point arithmetic operations in the computation process.
原因是这些快速算法虽减少了计算复杂性，但在计算过程中仍需要大量浮点运算。
Generally, modern processors do not have narrower floating-point than 32-bit arithmetic.
一般来说，现代处理器没有窄比32位浮点算术。
As an example we will write a small program to read and evaluate arithmetic expressions consisting of floating point Numbers, parentheses and the usual operators for addition, subtraction, and so on.
作为例子，我们将要写一个计算由浮点数、圆括号及一些常用的加、减等算术符号组成的算术表达式的程序。
To analyze the rounding error of calculator floating-point Numbers arithmetic operation is the foundation of numerical calculation method's error analysis.
对计算机浮点数算术运算的舍入误差进行分析，是对数值计算方法作误差分析的基础。
Floating-point (FP) arithmetic is usually used in many high accuracy calculation fields.
在很多高精度计算场合需要采用浮点运算。
As an example we will write a small program to read and evaluate arithmetic expressions consisting of floating point Numbers, parentheses and the usual operators for addition, subtraction, and so on.
例如我们要写一个计算由浮点数、圆括号及一些常用的加、减等算术符号组成的算术表达式的程序。
Block floating-point arithmetic is used to enhance the dynamic range and computation accuracy.
采用块浮点算法以提高动态范围和运算精度。
Some have provisions for loading and storing 16-bit floating-point objects, but they convert them to 32-bit objects as they are loaded and do arithmetic with 32-bit objects.
有些规定加载和存储16位浮点对象，但他们将它们转换为32位与32位加载和做算术的对象的对象。
Some have provisions for loading and storing 16-bit floating-point objects, but they convert them to 32-bit objects as they are loaded and do arithmetic with 32-bit objects.
有些规定加载和存储16位浮点对象，但他们将它们转换为32位与32位加载和做算术的对象的对象。
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