It just throws the decimal point away and that「s because, again, these are ints and the answer intuitively should be a floating point value, but I need to be more specific.

它直接把小数点后面的数值丢弃掉了，因为，凭直觉，那些整型数和结果应该是一个浮点数据，但是我需要一个更精确的数值。

If you」re reading a floating point number, there are other concerns.

如果您读取的浮点数，还有另外需要关注的问题。

We need to be accurate to two decimal places (e.g. in a simple accounting package), scale everything up by 100, and convert it back to floating point as late as possible.

我们处理小数的时候要精心些，比方说我们在做一个简单的统计程序时，要限制结果不能超过100，要尽可能晚的把它转化成浮点数。

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。

So what that means is the compiler is actually going to first 「cast」 so to speak 13 from whatever it is - to a float -- to a floating point value -- and then perform the division for us.

所以这里的意思是编译器将,做「计算“，譬如13这样一个浮点数,-到另一个浮点数-,然后为我们处理除法。

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。

Oh, and the fact that you can「t use the floating point processor directly to calculate transcendental functions (it」s done in software instead).

最后我们发现无法使用浮点处理器直接计算超函数（结果使用软件实现了）...

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。

To fix the problem, users can download patches for those versions or recompile PHP with additional flags for handling floating point digits.

要修复此问题，用户可下载上述版本的漏洞补丁，或是为处理浮点数使用额外标志重编译PHP 程序。

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。

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？

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。

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标準，该标準为32位浮点和64位双精度浮点二进制小数定义了二进制标準。

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，都支持使用标準的数学操作符（+、-、/和 *），可以在算式中混合使用浮点数和整数。

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。

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。

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。

It lacked floating point and parallel processing ability.

它缺少浮点和并行处理功能。

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。

A physical processor is organized as different execution units at the hardware level, for example, fixed point and floating point operation units.

在硬件级别中，物理处理器是作为不同的执行单元进行组织的，例如定点和浮点操作单元。

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。

You can run into issues of things like overflow, underflow, with floating point numbers and when you see a whole bunches of ones, it's particularly a good time to be suspicious.

来看看哪儿会出问题，你可能会碰到浮点数中的溢出和下溢问题,当你碰到一系列这种问题后，可能就会适时的开始怀疑结果的正确性。

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。