也许下面的例子有助于更清楚地理解这一点: 在java中，当我们使用查找任何操作的精确信息时，例如。 如果我们做double num1 = 10e+102;Double num2 = 8e+10; 结果= num1+ num2;

        The output will be so long and not precise, becasue it is precissed by the hardware e.g JVM and JIT has the license 
        as long as we dont have specify it Strictfp

Marking it Strictfp will make the result Uniform on every hardware and platform, because its precised value will be same
One scenario I can see is in a distributed application (or multiplayer game) where all floating-point calculations need to 
be deterministic no matter what the underlying hardware or CPU is.

Java 17更新

strictfp的用例集非常狭窄，从Java 17开始，它的功能已经被删除了。它仍然是一个有效的修饰符，但是现在strictfp什么都不做(JLS来源)。

相反，现在所有浮点操作都是严格的，就像在Java 1.2中引入strictfp之前一样。在现代处理器上，不再有任何额外的性能成本。

原来的答案

以下是一些参考资料:

Using strictfp (JDC Tech Tip) jGuru: What is the strictfp modifier for? When would I consider using it? Basically, what it all boils down to is whether or not you care that the results of floating-point expressions in your code are fast or predictable. For example, if you need the answers that your code comes up with which uses floating-point values to be consistent across multiple platforms then use strictfp. strictfp - Java Glossary Floating point hardware calculates with more precision, and with a greater range of values than the Java specification requires. It would be confusing if some platforms gave more precision than others. When you use the strictfp modifier on a method or class, the compiler generates code that adheres strictly to the Java spec for identical results on all platforms. Without strictfp, is it is slightly laxer, but not so lax as to use the guard bits in the Pentium to give 80 bits of precision. And finally the actual Java Language Specification, §15.4 FP-strict Expressions: Within an FP-strict expression, all intermediate values must be elements of the float value set or the double value set, implying that the results of all FP-strict expressions must be those predicted by IEEE 754 arithmetic on operands represented using single and double formats. Within an expression that is not FP-strict, some leeway is granted for an implementation to use an extended exponent range to represent intermediate results; the net effect, roughly speaking, is that a calculation might produce "the correct answer" in situations where exclusive use of the float value set or double value set might result in overflow or underflow.

不过我个人从来没用过它。

Strictfp确保在每个平台上从浮点计算中得到完全相同的结果。如果不使用strictfp, JVM实现可以在可用的地方使用额外的精度。

来自JLS:

Within an FP-strict expression, all intermediate values must be elements of the float value set or the double value set, implying that the results of all FP-strict expressions must be those predicted by IEEE 754 arithmetic on operands represented using single and double formats. Within an expression that is not FP-strict, some leeway is granted for an implementation to use an extended exponent range to represent intermediate results; the net effect, roughly speaking, is that a calculation might produce "the correct answer" in situations where exclusive use of the float value set or double value set might result in overflow or underflow.

换句话说，就是要确保Write-Once-Run-Anywhere实际上意味着write - once - get - equal - wrong - results - everywhere。

使用strictfp，您的结果是可移植的，没有它，它们更可能是准确的。

'strictfp'关键字用于强制Java中的浮点计算(float或double)的精度显式符合IEEE的754标准。如果不使用strictfp关键字，浮点精度取决于目标平台的硬件。

如果接口或类使用strictfp声明，那么该接口或类中的所有方法和嵌套类型都是隐式strictfp。

参考链接

实际上，维基百科上有一篇关于strictfp的好文章，其中有一个指向Java规范中浮点类型、格式和值部分的链接。

字里行间的意思是，如果您没有指定strictfp，那么JVM和JIT编译器就有权按它们想要的方式计算您的浮点计算。为了提高速度，它们很可能将计算委托给您的处理器。在使用strictfp时，计算必须符合IEEE 754算术标准，这在实践中可能意味着JVM将执行计算。

那么为什么要使用strictfp呢?我能看到的一个场景是在分布式应用程序(或多人游戏)中，无论底层硬件或CPU是什么，所有浮点计算都需要是确定的。代价是什么?很可能是执行时间。

一切都始于一个故事，

When java was being developed by James Gosling, Herbert and rest of his team. They had this crazy thing in mind called platform independency. They wanted to make oak(Java) so much better that it would run exactly same on any machine having different instruction set, even running different operating systems. But, there was a problem with decimal point numbers also known as floating point and double in programming languages. Some machines were built targeting efficiency while rest were targeting accuracy. So, the later(more accurate) machines had size of floating point as 80 bits while the former(more efficient/faster) machines had 64 bit doubles. But, this was against there core idea of building a platform independent language. Also, this might lead to loss of precision/data when a code is built on some machine(having double of 64 bit size) and run on another kind of machine(having double of 80 bit size).

扩大规模是可以容忍的，但缩小规模是不能容忍的。 因此，他们遇到了strictfp的概念，即严格浮点数。如果在类/函数中使用此关键字，则其浮点数和双精度浮点数在任何机器上都具有一致的大小。即分别为32位/64位。