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？
And I can fix this just by changing one of those values to a floating point.
所以我可以通过改变其中一个，整型数为浮点数。
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。
Spu_mul handles floating point multiplication (single and double precision).
spu_mul处理浮点乘法(单精度和多精度)。
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。
Other ranges can be used, but in my experience floating point numbers work best.
您也可以使用其它的范围的数，但是我的经验告诉我，浮点数是最有效的。
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。
Floating point Numbers are not exact, and manipulating them will result in rounding errors.
浮点数不是精确值，所以使用它们会导致舍入误差。
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 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。
They support Numbers of different types (integers and floating point), characters, strings, and so on.
它们支持许多不同的类型(整型和浮点型)、字符、字符串等等。
However, note that the payload looks very strange for a payload that simply returns a floating point number.
但请注意，这个有效负载看起来非常奇怪，不象是只返回一个浮点数的有效负载。
Floating point is a very similar concept, except that computers use binary rather than decimal as their base.
浮点是一个非常类似的概念，除了计算机使用二进制而不是十进制作为基础。
So both of these stories involve floating point values, but only in this case am I actually allocating memory.
所以这两个故事都涉及到浮点类型，但是只有那样我们才能真正地分配到内存。
This is, of course, not always possible, but you should be aware of the limitations of floating point comparison.
当然，这并不总是可能的，但您应该意识到要限制浮点数比较。
Operations for floating point variables are limited to simple assignment expressions and as arguments to VUE functions.
对于浮点变量的操作只限于简单的赋值表达式和作为VUE函数的变量。
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。
An attempt was made to execute a floating point instruction when the floating point available bit in the MSR (machine status register) was disabled.
如果在MSR(机器状态寄存器)中可用的浮点位被禁用，将尝试执行一个浮点指令。
These are based on the IEEE 754 standard, which defines a binary standard for 32-bit floating point and 64-bit double precision floating point binary-decimal Numbers.
它们都依据IEEE 754标准，该标准为32位浮点和64位双精度浮点二进制小数定义了二进制标准。
The standard mathematical operators, +, -, /, * are supported on both integer and floating point values, and you can mix and match floating-point and integers in calculations.
在整数和浮点数上，都支持使用标准的数学操作符（+、-、/ 和 *），可以在算式中混合使用浮点数和整数。
Floating point and decimal Numbers are not nearly as well-behaved as integers, and you cannot assume that floating point calculations that "should" have integer or exact results actually do.
浮点数和小数不象整数一样“循规蹈矩”，不能假定浮点计算一定产生整型或精确的结果，虽然它们的确“应该”那样做。
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。
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