The Fortran Language

Last updated on 2024-08-13 | Edit this page

Estimated time: 20 minutes

Overview

Questions

  • What are the strengths and weaknesses of Fortran?

Objectives

  • Analyze the merits of Fortran.
  • Learn how to compile and run a Fortran program.
  • Identify source files for different versions of Fortran.

This Fortran section can certainly be skipped if time is limited. If so, I’d suggest at least saying a few lines from the first paragraph below.

Modern Fortran

Fortran is one of the oldest computer languages used for scientific computing. One reason it is still heavily used is for historic reasons since there are just so many lines of Fortran code out there that are hard to replace. The good news is that this code base is extremely efficient. The Fortran language has also continued to modernize adding much of the same advanced functionality of C++.

Historically people used the f77 or Fortran 77 standard for a long time (defined in 1977). Modern Fortran has made great strides from this old standard in adding object oriented programming capabilities and a less stringent form. Fortran code is identified by the .f suffix or .F if there are pre-processor commands embedded. You may also see the .f90 or .f95 suffix to denote that the code adheres to the fluid formatting of the Fortran 90 and 95 standard defined in 1990 and 1995 respectively. Files with the .mod suffix are modules.

All these newer standards have added capabilities similar to C++ like dynamic memory allocation, object-oriented programming, and operator overloading. More recent work has been geared toward adding more parallel capabilities like the Coarray Fortran parallel execution model and the Do concurrent construct for identifying that a loop has no interdependencies and is therefore capable of being parallelized.

The primary value of Fortran will always be its efficiency and the same access to all the scientific and mathematical packages shared with C/C++. It is a column-major language like R and Matlab, and starts arrays at one instead of zero just like both of those as well. OpenMP and MPI packages likewise have full support for Fortran.

So Fortran is every bit as powerful and efficient as C/C++, but it is slowly being taken over by C/C++ on large supercomputers.

Language characteristics to avoid (gotchas)

While most memory in C/C++ is dynamically allocated, it is very common to have Fortran arrays statically allocated to a given size especially in older codes. This memory comes from what internally is called the stack which is a variable defined on each system. In our cluster at Kansas State University the default stack size as seen by doing ulimit -a is set to only a little over 8 MB while data arrays can easily exceed gigabyte sizes at times. When you exceed the stack size, your job crashes with a segfault that will not give you any useful information on what went wrong. If you think the stack size may be an issue, you can include a command ulimit -s unlimited before running your application to remove the stack size limit entirely.

Compiling Fortran code

Fortran is compiled with many of the same arguments and libraries used for C/C++. The Gnu version is gfortran and the Intel compiler is ifort. When using the OpenMP multi-threading package you will add the -fopenmp or -openmp flag respectively. To compile a Fortran MPI code you will use mpifort.

While there are many compilation options for each of these, you can general get by with -O3 level 3 optimization. I also strongly suggest always compiling with -g. This creates a symbol table so that if your code crashes you at least get the line number where it failed. Without this you get a pretty meaningless onslaught of information that won’t really give you a clue as to the problem. Compiling with -g should not slow down your code as long as you also use -O3, and the extra size of the executable should not matter.

Compiling source code to get an executable is again a two step process where source code is compiled into binary object files which are combined with any libraries needed in the linking stage. For simple applications this may all be done in a single step. For more complex codes involving many individual source files and modules it is common to have a Makefile handle everything. The Makefile provides the logic to compile only the parts of an application code base that have changed, then link everything together to produce the executable.

Practice compiling and running Fortran codes

Try compiling the dot_product_fortran.f90 code with the gfortran compiler, then try with ifort if you have access to it. Do the same with the optimized code dot_product_fortran_opt.f90 to see the difference that the built-in dot_product( x, y ) function can have. You can then compile the OpenMP version dot_product_fortran_openmp.f90 and do a scaling study, and if you are on a system with MPI installed then try compiling and running the MPI version dot_product_fortran_mpi.f90 using mpifort. Once you have compiled these codes manually take a look at the Makefile. This contains all the commands necessary to compile all the codes above with a single command make all_fortran.

Each computer system may be set up differently with regard to what compilers are available and how they need to be accessed. You may need to contact your administrator for help if you are unsure whether you have the Intel compiler suite installed, and how to access an MPI package if available. In my tests on a modern Intel system the raw Fortran code and optimized both took 0.14 seconds as did the OpenMP using 1 thread and the MPI version using 1 task. OpenMP using 4 threads took 0.063 seconds which is a little more than twice as fast and then performance flattened out for more threads. The MPI version using 4 tasks took 0.05 seconds which is slightly better than the OpenMP version, and 0.027 seconds for 8 tasks showing better scaling.

Key Points

  • Learn about the characteristics of modern Fortran