Uniform random number generator (RNG)

[Carter2x]

I am writing a program in Fortran language, in which i use a Random Number Generator (RNG) subroutine.
Until now i used ran1 from " Numerical Recipes in fortran ,second edition, Page 271 ", but i think it is quite slow.
While searching on the internet for a faster RNG i found a few Fortran subroutines
(many were in other languages C/C++ ...), but they were slower than the one i had.
Do you know were can i find a fast and quite longperiod (>= 10^12) RNG ?

 

[CRGreathouse]

Mersenne Twister

 

[Louisa]

I am writing a program in Fortran language, in which i use a Random Number Generator (RNG) subroutine.
Until now i used ran1 from " Numerical Recipes in fortran ,second edition, Page 271 ", but i think it is quite slow.
While searching on the internet for a faster RNG i found a few Fortran subroutines
(many were in other languages C/C++ ...), but they were slower than the one i had.
Do you know were can i find a fast and quite longperiod (>= 10^12) RNG ?

Have you tried using the one that's part of the language?

George Marsaglia developed a number of PRNGs.
Some produce integers directly, others produce floating-point values directly.

 

[D H]

Until now i used ran1 from " Numerical Recipes in fortran ,second edition, Page 271 ", but i think it is quite slow.

ran1 is fast, but it is lousy (it has way too short a period). What makes you think it is slow?

You can have fast or you can have good. Asking for both at once is a bit much.

 

[LudusRex]

I understand the importance of using a reliable and efficient random number generator (RNG) in your program. While ran1 from Numerical Recipes in Fortran may be a commonly used subroutine, it is understandable that you are looking for a faster option.

There are several factors to consider when choosing an RNG, including speed, period length, and randomness. It is important to balance these factors to ensure that your results are both accurate and efficient.

One way to find a suitable RNG is to consult established scientific literature and research. There are many studies and comparisons of different RNG algorithms that have been published in scientific journals. These studies often include performance evaluations, such as speed and period length, which can help you make an informed decision.

Another option is to look for RNG libraries specifically designed for scientific computing. These libraries are often optimized for speed and have been extensively tested and validated by the scientific community. Some popular examples include the GNU Scientific Library and the Random123 library.

In addition, some hardware manufacturers also offer fast and reliable RNGs, such as Intel's Random Number Generator Instruction (RDRAND) and AMD's Secure Random Number Generator (SRNG). These options may require additional setup and configuration, but they can provide high-quality random numbers with long periods.

Overall, I recommend doing thorough research and considering all factors before choosing an RNG for your program. It is also important to properly test and validate your results to ensure the accuracy and reliability of your simulations or calculations.