Fortran to CUDA Porting Biochemical code to GPU

Dear Sir,

I need some help for any member from this site.I have some fortran code …I want to porting this code in GPU using CUDA.I am unable to do this.Plz if possible to do this for me anyway…I will attached my rar file .

test.tar.gz (90 KB)

Hi, in order to do that you first need to identify if your fortran code is actually suitable for CUDA implementation? Do you want to pass the whole code or only an interesting part that is CPU time expansive?

As far as i know, the most simple and free solution to transfer Fortran Code to CUDA is to use a Wrapper (since Fortran can call C functions, it can call CUDA functions as well, you just have link CUDA include/lib to your project). Therefore you can develop your code in CUDA C and put it in a ‘call-able way’ then you can replace your parallelized fortran subroutines with a call to the CUDA C developped function.

Hope that will help you, several tips on that can be found on internet.

Maybe i’m also wrong on a part of my explaination,



I second what Pascal has said.
Beyond that, there are two commercial solutions from PGI that allow you to either write your CUDA kernels in Fortran or allow you to convert existing (or write new) Fortran code to CUDA by just inserting directives.

Disclaimer: I have no experience with these products, nor am I associated with PGI in any way.


Thanks…My fortran code is mainly suitable for Cuda implementation.I want whole code part…of this code.




Once again please if possible to give me a version of cuda code against my fortran code



How much are you willing to pay for this service?


So far, your code includes several different files related to particle sim and maths. If you want someone to pass it on CUDA you will have to pay i think, because its not a trivial solution that can be implemented straightforward in 2 hours (or maybe it can). Even if it looks feasible (datas are quite small & I guess you can do all the calculus on GPU side without considering any asynchronous mechanism - unoptimal solution).

Good luck, its an opportunity for you to begin in cuda GPGPU