Fortran vs C++, does Fortran still hold any advantage in numerical analysis these days?

With the rapid development of C++ compilers,especially the intel ones, and the abilities of directly applying SIMD functions in your C/C++ codes, does Fortran still hold any real advantage in the world of numerical computations?

I am from an applied maths background, my job involves a lot of numerical analysis, computations, optimisations and such, with a strictly defined performance-requirement.

I hardly know anything about Fortran, I have some experience in C/CUDA/matlab(if you consider the latter as a computer language to begin with), and my daily task involves analysis of very large data (e.g. 10GB-large matrix), and it seems the program at least spend 2/3 of its time on memory-accessing (thats why I send some of its job to GPU), do you people think it may worth the effects for me to trying the fortran routine on at least some performance-critical part of my codes to improve the performance of my program?

Because the complexity and things need to be done involved there, I will only go that routine if only there is significant performance benefit there, thanks in advance.

Fortran has strict aliasing semantics compared to C++ and has been aggressively tuned for numerical performance for decades. Algorithms that uses the CPU to work with arrays of data often have the potential to benefit from a Fortran implementation.

The programming languages shootout should not be taken too seriously, but of the 15 benchmarks, Fortran ranks #1 for speed on four of them (for Intel Q6600 one core), more than any other single language. You can see that the benchmarks where Fortran shines are the heavily numerical ones:

• spectral norm 27% faster
• fasta 67% faster
• mandelbrot 56% faster
• pidigits 18% faster

Counterexample:

• k-nucleotide 500% slower (this benchmark focuses heavily on more sophisticated data structures and string processing, which is not Fortran's strength)

You can also see a summary page "how many times slower" that shows that out of all implementations, the Fortran code is on average closest to the fastest implementation for each benchmark -- although the quantile bars are much larger than for C++, indicating Fortran is unsuited for some tasks that C++ is good at, but you should know that already.

So the questions you will need to ask yourself are:

1. Is the speed of this function so critical that reimplementing it in Fortran is worth my time?

2. Is performance so important that my investment in learning Fortran will pay off?

3. Is it possible to use a library like ATLAS instead of writing the code myself?

Answering these questions would require detailed knowledge of your code base and business model, so I can't answer those. But yes, Fortran implementations are often faster than C++ implementations.

Another factor in your decision is the amount of sample code and the quantity of reference implementations available. Fortran's strong history means that there is a wealth of numerical code available for download and even with a trip to the library. As always you will need to sift through it to find the good stuff.

The complete and correct answer to your question is, "yes, Fortran does hold some advantages".

C++ also holds some, different, advantages. So do Python, R, etc etc. They're different languages. It's easier and faster to do some things in one language, and some in others. All are widely used in their communities, and for very good reasons.

Anything else, in the absence of more specific questions, is just noise and language-war-bait, which is why I've voted to close the question and hope others will too.

Fortran is just naturally suited for numerical programming. You tend to have a large amount of numbers in such programs, typically arranged arrays. Arrays are first class citizens in Fortran and it is often pretty straight forward to translate numerical kernels from Matlab into Fortran. Regarding potential performance advantages see the other answers, that cover this quite nicely. The baseline is probably you can create highly efficient numerical applications with most compiled languages today, but you might jump through some loops to get there. Fortran was carefully designed to allow the compiler to recognize most spots for optimizations, due to the language features. Of course you can also write arbitrary slow code with any compiled language, including Fortran. In any case you should pick the tools as suited. Fortran suits numerical applications, C suits system related development. On a final remark, learning Fortran basics is not hard, and it is always worthwhile to have a look into other languages. This opens a different view on problems you want to solve.

Also worth mentioning is that Fortran is a lot easier to master than C++. In fact, Fortran has a shorter language spec than plain C and it's syntax is arguably simpler. You can pick it up very quickly.

Meaning that if you are only interested in learning C++ or Fortran to solve a single specific problem you have at the moment (say, to speed up the bottlenecks in something you wrote in a prototyping language), Fortran might give you a better return on investment.

Fortran code is better for matrix and vector type operation in general. But you also can produce similar performance with c/c++ code by passing hints/suggestions to the compiler to produce similar quality vector instructions. One option that gave me good boost was not to assume memory aliasing among input variables that are array objects. This way, the compiler can aggressively do inner loop unrolling and pipelining for ILP where it can overlap loads and store operation across loop iteration with right prefetches.