Don't use floating point Numbers for exact values.
不要用浮点值表示精确值。
It lacked floating point and parallel processing ability.
它缺少浮点和并行处理功能。
The layout of IEEE floating point values is shown in Figure 1.
ieee浮点值的格式如图1所示。
Why do you need separate macros for floating point comparisons?
为什么需要用单独的宏进行浮点数比较？
The signed/unsigned keyword is required for non-floating point declarations.
signed/unsigned关键字是声明非浮点类型必需的。
In future articles, I plan to take a closer look at floating point workloads.
在未来的文章中，我计划仔细研究一下浮点工作负载。
Google provides the macros shown in Listing 9 for floating point comparisons.
Google提供清单9所示的宏以支持浮点数比较。
One of the trickiest checks in regression setups is doing floating point comparisons.
回归测试中最棘手的检查之一是浮点比较。
When the kernel is executing floating point instructions, the FPU state is not saved.
当内核在执行浮点指令时，FPU状态不被保存。
There we have merely 8 bytes for the two floating point values for the X and Y dimension.
对于X和Y维，我们只有用于两个浮点值的8个字节。
Supported variables include integers, floating point Numbers, strings, arrays, and objects.
支持的变量包括整型、浮点型的数字、字符串、数组和对象。
IEEE 754 represents floating point Numbers as base 2 decimal Numbers in scientific notation.
IEEE 754用科学记数法以底数为2的小数来表示浮点数。
You can read integer, floating point, and string values that are all defined as' scalar 'objects.
您可以读取被定义为“标量”的整型、浮点型和字符串值。
It is simply a textual representation of the value according to the IEEE floating point standard.
根据IEEE浮点标准，它只是值的文本表示。
Long double IEEE double extended floating point exponent of 15 bits and signed fraction of 64 bits.
longdoubleIEEE双字节扩展浮点数15位指数，64位带符号小数。
You should always specify the precision of floating point variables when writing custom shaders.
编写自定义着色器时，您应该始终指定浮点变量的精度。
It had separate floating point registers and could scale from the low - to the high-end workstations.
它有单独的浮点寄存器，可以从低端工作站扩展到高端工作站。
Those topics deal with floating point and vector processing and are outside the scope of this article.
这些主题涉及的是浮点和向量处理，已经超出了本文的范围。
Otherwise, if either argument is a floating point number, the other is converted to floating point.
否则，如果其中一个参数是浮点数，另一个也要转换成浮点数。
The non-ordered nature of NaN adds further opportunities for error when comparing floating point numbers.
NaN的无序性质使得在比较浮点数时更容易发生错误。
CPhase takes a (classical) floating point number as its first argument and a qubit as it's second argument.
CPhase的第一个参数是(经典的)浮点数，第二个参数是一个量子位。
An SPU USES vector operations itself and can thereby execute up to eight floating point instructions per clock cycle.
SPU本身使用向量操作，每个时钟周期可以执行多达8条浮点指令。
But on a register-based system, the hexadecimal value is read from an integer register, not the floating point register.
但是在基于寄存器的系统中，这个16进制的值会从一个整数寄存器中读取，而不是从浮点寄存器中读取。
These include compile flags dealing with relocation, table of contents sizes, floating point options, bit alignment, and so on.
这些标记包括处理重定位、目录(tableofcontents)大小、浮点选项、位调整等等的编译标记。
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。
In Part 2, I'll explore the functions more designed for operating on floating point Numbers as opposed to abstract real Numbers.
在第2部分，我将探讨专为操作浮点数(与抽象实数相反)而设计的函数。
Microwulf has been measured to process 26.25 gigaflops, or 26.25 billion double-precision floating point instructions, per second.
Microwulf经测试，运行能力为每秒26.25gigaflops，相当于每秒能进行260多亿次的双精度浮点运算。
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。
The solution is to cast the address of the floating point variable to a pointer to an int, which is then de-referenced as shown below.
解决方案是将浮点变量的地址强制转换成一个指向整型类型的指针，如下所示。
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。
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