blocks|key|2059221|text|在大多数桌面应用程序上，是这样的。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2059222|但是，如果你有一个巨大的数组，如果你不需要精度，那么将它的大小减半可能是值得的。|2059223|特别是考虑到几乎所有的消费者桌面都有在硬件中完成的双精度浮点运算。|2059224|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|H|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|I|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|J|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|G|$]]

On most desktop applications, yes.

Though if you have a huge array of them, halving the size of that could be significant enough to be worthwhile if you don't need the precision.

Especially given that pretty much all consumer desktops have double-precision floating-point arithmetic done in hardware.

blocks|key|651218|text|摘自this+.net文章|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|651219|651220|数据类型宽度|blockquote|651221|651222|最有效的数据类型是那些使用运行时平台的本机数据宽度的数据类型。在当前的平台上，计算机和操作系统的数据宽度均为32位。|651223|因此，在Visual+Basic+.NET中，整数是目前最有效的数据类型，其次是长整型、短整型和字节型。您可以通过关闭整数溢出检查来提高Short和Byte的性能，例如通过设置RemoveIntegerChecks属性，但这会导致由于未检测到的溢出而导致计算不正确的风险。您不能在运行时打开和关闭此检查；只能在应用程序的下一次构建时设置它的值。|651224|如果您需要小数值，最好的选择是Double，因为当前平台的浮点处理器以双精度执行所有操作。其次是Single和Decimal，按效率顺序排列。|651225|entityMap|0|LINK|mutability|MUTABLE|url|http://msdn.microsoft.com/en-us/library/aa289513%2528VS.71%2529.aspx^0|2|4|0|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@]|9|@$A|X|B|Y|1|Z]]|C|$]]|$1|D|3|-4|5|6|7|10|8|@]|9|@]|C|$]]|$1|E|3|F|5|G|7|11|8|@]|9|@]|C|$]]|$1|H|3|-4|5|6|7|12|8|@]|9|@]|C|$]]|$1|I|3|J|5|6|7|13|8|@]|9|@]|C|$]]|$1|K|3|L|5|6|7|14|8|@]|9|@]|C|$]]|$1|M|3|N|5|6|7|15|8|@]|9|@]|C|$]]|$1|O|3|-4|5|6|7|16|8|@]|9|@]|C|$]]]|P|$Q|$5|R|S|T|C|$U|V]]]]

From <a href="http://msdn.microsoft.com/en-us/library/aa289513%28VS.71%29.aspx" rel="noreferrer">this</a> .net article

<blockquote>
 Data Type Width
 
 The most efficient data types are
 those that use the native data width
 of the run-time platform. On current
 platforms, the data width is 32 bits,
 for both the computer and the
 operating system.
 
 Consequently, Integer is currently the
 most efficient data type in Visual
 Basic .NET. Next best are Long, Short,
 and Byte, in that order of efficiency.
 You can improve the performance of
 Short and Byte by turning off integer
 overflow checking, for example by
 setting the RemoveIntegerChecks
 property, but this incurs the risk of
 incorrect calculations due to
 undetected overflows. You cannot
 toggle this checking on and off during
 run time; you can only set its value
 for the next build of your
 application.
 
 If you need fractional values, the
 best choice is Double, because the
 floating-point processors of current
 platforms perform all operations in
 double precision. Next best are Single
 and Decimal, in that order of
 efficiency.
</blockquote>

blocks|key|1892412|text|正如Mark在他的评论中所说，在内存受限的系统中，空间可能是一个问题。您可能还想对列表进行索引或排序，如果您可以将值存储在单元值中，为什么还要对双精度值进行索引或排序？|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1892413|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|D|8|@]|9|@]|A|$]]|$1|B|3|-4|5|6|7|E|8|@]|9|@]|A|$]]]|C|$]]

As Mark says in his comment, space can be an issue on memory-constrained systems. You may also want to index or sort a list, and why do that on doubles if you can store your values in singles?

blocks|key|1894588|text|在某些硬件上，涉及双值的算术可能比涉及单值的算术花费更长的时间，但大多数最新的FPU具有单一的本机数据类型(例如，x86的80位扩展浮点值)，无论您使用的是哪种内存数据类型，该数据类型都将在内部用于计算。所以这就是说，“单精度的FPU计算将会更快”通常是而不是，这是今天在大多数现代硬件上使用单精度的原因。|type|unstyled|depth|inlineStyleRanges|offset|length|style|BOLD|entityRanges|data|1894589|也就是说，除了在其他答案中阐述的“使用更少的内存”的原因之外，当涉及到像SSE和AltiVec这样的单精度向量指令时，还有一个非常实际的原因-单精度是喜欢比双精度快一倍的速度，因为指令操作固定大小的向量，并且您可以将两倍的单精度值填充到单个向量中，而处理时间通常保持不变。|1894590|例如，使用能够在2个时钟周期内处理矢量乘法的128位矢量单元，您可以获得每个时钟2个单精度乘法的吞吐量，而不是1个双精度，因为您可以在一个矢量中使用4个单精度运算，而不是2个双精度运算。|1894591|内存带宽也会产生类似的影响，而且不是特定于向量处理-如果您有大量的双精度数组，它们不仅占用两倍的空间，而且在您的算法受到带宽限制的情况下，可能需要两倍的时间进行处理(考虑到向量处理单元的大小不断增加和延迟不断减少，这种情况的可能性越来越大)。|1894592|entityMap^0|3J|3|0|0|0|0^^$0|@$1|2|3|4|5|6|7|N|8|@$9|O|A|P|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|Q|8|@]|D|@]|E|$]]|$1|H|3|I|5|6|7|R|8|@]|D|@]|E|$]]|$1|J|3|K|5|6|7|S|8|@]|D|@]|E|$]]|$1|L|3|-4|5|6|7|T|8|@]|D|@]|E|$]]]|M|$]]

On some hardware, arithmetic involving double values may take longer than that involving single values, but most recent FPUs have a single native data type (e.g., 80-bit extended floating point values for x86) which will be used internally for calculations regardless of what in-memory data type you are using. So that is to say that "FPU calculations will be faster with single precision" is generally not a reason to use single-precision on most modern hardware today.

That said, in addition to the "uses less memory" reasons elaborated on in the other answers, there is a very practical reason when it comes to SIMD vector instructions like SSE and AltiVec - single precision is likey to be twice as fast as double precision, since the instructions operate on vectors of fixed size, and you can stuff twice as many single precision values into a single vector, with the processing time typically remaining the same.

For example, with a 128-bit vector unit capable of processing vector multiplications in 2 clock cycles, you could get a throughput of 2 single precision multiplications per clock, versus 1 double precision, since you can 4 singles in a vector, versus two doubles.

A similar effect occurs with memory bandwidth, and is not specific to vector processing - if you have large arrays of doubles, they will not only take twice the space, but may take up to twice as long to process in the case that your algorithm is bandwidth constrained (which is increasingly likely given the increasing sizes and decreasing latencies of vector processing units).

blocks|key|591835|text|双精度需要更多的空间，但额外的精度可能是必要的，也可能不是必要的。我在浮点运算非常普遍的科学世界里做了很多编程，发现通常你可以以双倍或更高的精度进行计算，但将结果存储为单精度，而不会产生不良影响。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|591836|请记住，一旦数字被吸进FPU，它们无论如何都会扩展到非常高的精度。也就是说，最好尝试在两种精度下执行任何操作，并查看结果是否具有可比性。|591837|不幸的是，计算仍然是一门实验科学。|591838|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|H|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|I|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|J|8|@]|9|@]|A|$]]|$1|F|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|G|$]]

Doubles take more space but the extra precision may or may not be necessary. I have done a lot of programming in the scientific world where floating point arithmetic is very common and have found that often you can do the calculations in double or higher precision but store the results as singles without ill effect.

Keep in mind that once the numbers are sucked into the FPU, they are expanded to very high precision anyway. That being said, it would be best to try whatever you are doing in both precisions and see if the results are comparable.

Unfortunately, computing is still an experimental science.

blocks|key|1892509|text|如果你正在编写OpenGL，那么使用GLSingle+(例如single)而不是GLDouble是很正常的。在几乎所有的情况下，单精度对于大多数图形应用程序来说已经足够了，而且应该更快-尽管我承认在最新一代的GPU上我不确定这一点。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1892510|关于这一点，我最喜欢的引用是，单一的精度足以导航到月球和返回，所以在实践中，这是不寻常的，导致真正的问题。也就是说，在大多数情况下，我现在会选择double，因为存储空间很便宜，而且不太可能出现任何奇怪的二进制到十进制的问题。|1892511|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|F|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|G|8|@]|9|@]|A|$]]|$1|D|3|-4|5|6|7|H|8|@]|9|@]|A|$]]]|E|$]]

If you're coding OpenGL then it's normal to use GLSingle (eg single) rather than GLDouble. In nearly all circumstances single precision is more than enough for most graphics applications and should be faster - although I confess I'm not certain of this on the latest generation of GPUs. 

My favourite citation on this is that single precision was sufficient to navigate to the moon and back, so in practise it's unusual to cause a real issue. That said in most circumstances I'd reach for a double nowadays as storage is cheap and there's less likely to be any odd binary to decimal issues.

Are there any situations where it would make more sense to use a single datatype instead of a double? From my searching, the disadvantage to a double is that it requires more space, which isn't a problem for most applications. In that case, should all floating point numbers be doubles?

A little background info:
I'm working with an application that deals with a lot of data about coordinates and chemicals. A few customers have noticed that when importing spreadsheets of data, some values with high precision are rounded down the precision of a single.

Single vs Double datatypes

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

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是否存在使用单一数据类型而不是双精度数据类型更有意义的情况？根据我的搜索，double的缺点是它需要更多的空间，这对于大多数应用程序来说不是问题。在这种情况下，所有的浮点数都应该是双精度的吗？一点背景信息:我正在使用一个应用程序，它处理大量关于坐标和化学物质的数据。一些客户已经注意到，在导入数据的电子表格时，一些高精度的值会四舍五入为单精度。

问单数据类型与双数据类型
EN

回答 6

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