我不时地读到Fortran在繁重的计算中比C更快。这是真的吗?我必须承认我几乎不懂Fortran,但是到目前为止我看到的Fortran代码并没有显示出该语言具有C语言所不具备的特性。

如果是真的,请告诉我原因。请不要告诉我什么语言或库适合处理数字,我不打算写一个应用程序或库来做这个,我只是好奇。


当前回答

在某种程度上,Fortran在设计时就考虑到了编译器优化。该语言支持整个数组操作,编译器可以利用并行性(特别是在多核处理器上)。例如,

密集矩阵乘法很简单:

matmul(a,b)

向量x的L2范数为:

sqrt(sum(x**2))

此外,FORALL、PURE和ELEMENTAL程序等语句进一步有助于优化代码。由于这个简单的原因,即使是Fortran中的指针也不如C语言灵活。

即将发布的Fortran标准(2008)具有协同数组,允许您轻松地编写并行代码。G95(开源)和来自CRAY的编译器已经支持它。

所以是的,Fortran可以很快,因为编译器可以比C/ c++更好地优化/并行化。但是就像生活中的其他事情一样,有好的编译器和坏的编译器。

其他回答

这两种语言具有相似的特性集。性能上的差异来自Fortran不允许混淆的事实,除非使用了EQUIVALENCE语句。任何有别名的代码都不是有效的Fortran,但是它是由程序员而不是编译器来检测这些错误的。因此,Fortran编译器忽略了可能的内存指针别名,并允许它们生成更有效的代码。看一下C语言中的这个小例子:

void transform (float *output, float const * input, float const * matrix, int *n)
{
    int i;
    for (i=0; i<*n; i++)
    {
        float x = input[i*2+0];
        float y = input[i*2+1];
        output[i*2+0] = matrix[0] * x + matrix[1] * y;
        output[i*2+1] = matrix[2] * x + matrix[3] * y;
    }
}

这个函数在优化后会比Fortran函数运行得慢。为什么如此?如果你在输出数组中写入值,你可能会改变矩阵的值。毕竟,指针可以重叠并指向相同的内存块(包括int指针!)C编译器被迫从内存中重新加载所有计算的四个矩阵值。

在Fortran中,编译器只加载一次矩阵值,并将它们存储在寄存器中。它可以这样做是因为Fortran编译器假定指针/数组在内存中不重叠。

Fortunately, the restrict keyword and strict-aliasing have been introduced to the C99 standard to address this problem. It's well supported in most C++ compilers these days as well. The keyword allows you to give the compiler a hint that the programmer promises that a pointer does not alias with any other pointer. The strict-aliasing means that the programmer promises that pointers of different type will never overlap, for example a double* will not overlap with an int* (with the specific exception that char* and void* can overlap with anything).

If you use them you will get the same speed from C and Fortran. However, the ability to use the restrict keyword only with performance critical functions means that C (and C++) programs are much safer and easier to write. For example, consider the invalid Fortran code: CALL TRANSFORM(A(1, 30), A(2, 31), A(3, 32), 30), which most Fortran compilers will happily compile without any warning but introduces a bug that only shows up on some compilers, on some hardware and with some optimization options.

Fortran和C语言在特定目的上并没有哪一种语言比另一种更快。对于每种语言的特定编译器,有些编译器比其他编译器更适合某些任务。

多年来,Fortran编译器一直存在,它可以对你的数字例程施黑魔法,使许多重要的计算变得异常快速。当代的C编译器无法做到这一点。因此,在Fortran中出现了许多伟大的代码库。如果您想要使用这些经过良好测试的、成熟的、出色的库,就需要使用Fortran编译器。

我的非正式观察表明,如今人们用任何古老的语言来编码他们繁重的计算内容,如果这需要一段时间,他们就会在一些廉价的计算集群上找到时间。摩尔定律让我们所有人都成了傻瓜。

没有一种语言比另一种语言更快,所以正确的答案是否定的。

你真正要问的是“用Fortran编译器X编译的代码是否比用C编译器Y编译的等效代码更快?”这个问题的答案当然取决于您选择哪两个编译器。

人们可能会问的另一个问题是“考虑到在他们的编译器中优化投入了相同的精力,哪个编译器会生成更快的代码?” 这个问题的答案实际上是Fortran。Fortran编译器有一些优势:

Fortran had to compete with Assembly back in the day when some vowed never to use compilers, so it was designed for speed. C was designed to be flexible. Fortran's niche has been number crunching. In this domain code is never fast enough. So there's always been a lot of pressure to keep the language efficient. Most of the research in compiler optimizations is done by people interested in speeding up Fortran number crunching code, so optimizing Fortran code is a much better known problem than optimizing any other compiled language, and new innovations show up in Fortran compilers first. Biggie: C encourages much more pointer use than Fortran. This drasticly increases the potential scope of any data item in a C program, which makes them far harder to optimize. Note that Ada is also way better than C in this realm, and is a much more modern OO Language than the commonly found Fortran77. If you want an OO langauge that can generate faster code than C, this is an option for you. Due again to its number-crunching niche, the customers of Fortran compilers tend to care more about optimization than the customers of C compilers.

然而,没有什么能阻止人们在C编译器的优化上投入大量精力,并使其生成比他们平台的Fortran编译器更好的代码。事实上,C编译器产生的较大销售额使得这种情况非常可行

我将Fortran、C和c++的速度与netlib中的经典Levine-Callahan-Dongarra基准进行了比较。使用OpenMP的多语言版本是 http://sites.google.com/site/tprincesite/levine-callahan-dongarra-vectors C语言更丑陋,因为它一开始是自动翻译,加上某些编译器的限制和pragmas插入。 c++就是在适用的地方使用STL模板的C。在我看来,STL在是否能提高可维护性方面好坏参半。

为了了解自动函数内联在多大程度上改进了优化,只需要进行很少的练习,因为示例基于传统的Fortran实践,其中很少依赖内联。

到目前为止使用最广泛的C/ c++编译器缺乏自动向量化,而这些基准测试严重依赖于此。

关于这之前的帖子:在Fortran中使用括号来指示更快或更准确的求值顺序的例子有两个。已知的C编译器没有在不禁用更重要的优化的情况下观察括号的选项。

Fortran速度更快有几个原因。然而,它们的重要性是如此无关紧要,或者可以通过任何方式解决,所以它不应该是重要的。现在使用Fortran的主要原因是维护或扩展遗留应用程序。

PURE and ELEMENTAL keywords on functions. These are functions that have no side effects. This allows optimizations in certain cases where the compiler knows the same function will be called with the same values. Note: GCC implements "pure" as an extension to the language. Other compilers may as well. Inter-module analysis can also perform this optimization but it is difficult. standard set of functions that deal with arrays, not individual elements. Stuff like sin(), log(), sqrt() take arrays instead of scalars. This makes it easier to optimize the routine. Auto-vectorization gives the same benefits in most cases if these functions are inline or builtins Builtin complex type. In theory this could allow the compiler to reorder or eliminate certain instructions in certain cases, but likely you'd see the same benefit with the struct { double re; double im; }; idiom used in C. It makes for faster development though as operators work on complex types in Fortran.