Fortran (Programming Language)
FORTRAN, C, and C++ have a long history as the basic/main compiled languages for high performance computing. The key parallel computing packages, MPI and OpenMP, have been implemented in all of them from the beginning. While C and C++ have been extended for all programming purposes, FORTRAN originated from FORmular TRANslation, and developed with an emphasis on scientific computing. After the FORTRAN I-IV, 66, and 77 stages, the FORTRAN 90, 95, 2003, 2008, and 2015 versions have adopted many advanced features to become a true modern (object oriented) programming language, especially geared toward scientific computations. The following lists some of the most useful and prominent programming features of FORTRAN.
FORTRAN is very well structured. All routines should have a clear beginning statement, and a corresponding ending one. For example (since case-in-sensitiveness, usually written in either lower or upper case only)
PROGRAM MY_VERY_USEFUL_CODE ... CALL PROBLEM_SOLVING (...) ... STOP END PROGRAM MY_VERY_USEFUL_CODE SUBROUTINE PROBLEM_SOLVING (...) ... RESULT = AVERAGE_SCORE (...) RETURN END SUBROUTINE PROBLEM_SOLVING FUNCTION AVERAGE_SCORE (...) ... RETURN END FUNCTION AVERAGE_SCORE
The DO loop and IF structure are also finished with an END statement.
DO I = ISTART, IEND ... END DO IF (CONDITION) ... ELSE ... END IF
Similar to classes in C++, modules are very important and widely-used in FORTRAN. Theoretically modules are not classes, but usually contain many objects, since in most scientific computations data structures are known and given objects. Modules can also contain specific routines operating on the objects inside, similar to the encapsulation concept of classes. Meanwhile modules are also a good method to share such objects, so that routines arguments can be reduced to necessaries only.
As a modern language, FORTRAN also supports routine overloading.
MODULE MY_KINETICS INTERFACE GENERIC_KINETIC SUBROUTINE KINETIC_ROUTINE_A(...) ... END SUBROUTINE KINETIC_ROUTINE_A SUBROUTINE KINETIC_ROUTINE_B(...) ... END SUBROUTINE KINETIC_ROUTINE_B SUBROUTINE KINETIC_ROUTINE_C(...) ... END SUBROUTINE KINETIC_ROUTINE_C ... END INTERFACE GENERIC_KINETIC END MODULE MY_KINETICS
After this module is cited
with each of the specific routines available, the call
will invoke the specific routine with the matching unique interface. In C++, overloading is a type of class polymorphism.
Most FORTRAN compilers have built-in data types of very high precision, like quadruple precision
REAL*16 :: VELOCITY(3,1000) COMPLEX*32 :: HAMILTON(1000, 1000)
Dynamic Memory Allocation
Early versions of FORTRAN had a big drawback: they did not allow for dynamic memory allocation, forcing re-compilation array sizes were changed. Newer versions of FORTRAN (since F90) support such operations even for many-dimensional arrays.
REAL*16, ALLOCATABLE :: COMPLICATED_DATA(:, :, :, :, :, :) ALLOCATE(COMPLICATED_DATA(3, 90, 80, 72, 500, 28))
in contrast to C/C++ where all arrays are allocated as one-dimensional.
User Defined Data Types
FORTRAN also supports user defined data types:
TYPE PERSON CHARACTER(LEN=10) :: NAME REAL :: AGE INTEGER :: ID END TYPE PERSON TYPE(PERSON) :: YOU, ME REAL :: DIFF YOU%ID = 12345 DIFF = YOU%AGE - ME%AGE
Some Other Features
Links and Further Redading
Standard debugging and profiling tools such as Sun Studio are designed for serial or multi-threaded programs. They do not handle multi-process runs very well.
Quite often, the best way to check the performance of an MPI program is timing it by insertion of suitable routines. MPI supplies a "wall-clock" routine called MPI_WTIME(), that lets you determine how much actual time was spent in a specific segment of your code. An other method is calling the subroutines ETIME and DTIME, which can give you information about the actual CPU time used. However, it is advisable to carefully read the documentation before using them with MPI programs. In this case, refer to the Sun Studio 12: Fortran Library Reference.
We also provide a package called the HPCVL Working Template (HWT), which was created by Gang Liu. The HWT provides 3 main functionalities:
- Maintenance of multiple versions of the same code from a single source file. This is very useful, if your MPI code is based on a serial code that you want to convert.
- Automatic Relative Debugging which allows you to use pre-existing code (for example the serial version of your program) as a reference when checking the correctness of your MPI code.
- Simple Timing which is needed to determine bottlenecks for parallelization, to optimize code, and to check its scaling properties.
The HWT is based on libraries and script files. It is easy to use and portable (written largely in Fortran). Fortran, C, C++, and any mixture thereof are supported, as well as MPI and OpenMP for parallelism. Documentation of the HWT is available. The package is installed on our clusters in /opt/hwt.
Send email to firstname.lastname@example.org. We have scientific programmers on staff who will probably be able to help you out. Of course, we can't do the coding for you but we do our best to get your code ready for parallel machines and clusters. |}