传统的X86结构利用浮动点单位进行浮动点计算。 邮联在其内部登记册中进行所有计算,每个登记册的精度为80比方。 每当你尝试使用<代码>float或double时,这些变量首先从记忆中填入万国邮联的内部登记册。 这意味着实际计算速度绝对没有变化,因为无论如何计算都是全方位进行的。 唯一可能不同的是,从记忆中提取价值的速度加快,并将结果重新储存到记忆中。 自然,在32个轨道平台上,与<代码>float相比,负荷/储存时间可能更长。 在64个轨道平台上,斜线没有任何区别。

Modern x86 architectures support extended instruction sets (SSE/SSE2) with new instructions that can perform the very same floating-point calculations without involving the "old" FPU instructions. However, again, I wouldn t expect to see any difference in calculation speed for float and double. And since these modern platforms are 64-bit ones, the load/store speed is supposed to be the same as well.

在不同的硬件平台上,情况可能有所不同。 但是,通常较小的浮动点类型不应提供任何性能效益。 较小的浮动点类型的主要目的是节省记忆,而不是改进业绩。

Edit: (To address @MSalters comment) What I said above applies to fundamental arithmetical operations. When it comes to library functions, the answer will depend on several implementation details. If the platform s floating-point instruction set contains an instruction that implements the functionality of the given library function, then what I said above will normally apply to that function as well (that would normally include functions like sin, cos, sqrt). For other functions, whose functionality is not immediately supported in the FP instruction set, the situation might prove to be significantly different. It is quite possible that float versions of such functions can be implemented more efficiently than their double versions.

你的第一个问题已经回答:here on SO。

您的第二个问题完全取决于你所处理数据的“大小”。 它 all倒了该系统的低水平结构及其如何处理大价值。 32个轨道系统中64个参数的数据需要2个周期才能进入2个登记册。 关于64个轨道系统的同样数据只能需要1个周期才能进入1个登记册。

一切都取决于你重新做什么。 我认为,没有快速而艰难的规则,因此,你需要分析目前的任务,选择最适合你对具体任务的需求。

从一些研究和经验衡量 我在 Java做了以下工作:

• basic arithmetic operations on doubles and floats essentially perform identically on Intel hardware, with the exception of division;
• on the other hand, on the Cortex-A8 as used in the iPhone 4 and iPad, even "basic" arithmetic on doubles takes around twice as long as on floats (a register FP addition on a float taking around 4ns vs a register FP on a double taking around 9ns);
• I ve made some timings of methods on java.util.Math (trigonometrical functions etc) which may be of interest -- in principle, some of these may well be faster on floats as fewer terms would be required to calculate to the precision of a float; on the other hand, many of these end up being "not as bad as you d think";

在某些情况下,例如,记忆带宽问题超过了“粗”计算时间,这也是事实。

虽然在大多数系统中,<代码>double<>/code>将与各个数值的<编码>float相同,但你还是认为,像<代码>sqrt、sin等电子计算功能在单一计算中应当比计算其双重计算要快得多。 在C99,即使您的变量为double<>/code>,也可使用sinf等功能。