Fortran 90 as a Language of Choice for Engineering Students
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   Dr. John Prentice of Quetzal Associates posted this email message
   (January 1995) explaining why engineering students should learn
   Fortran 90. Since he posted it, this message has been reposted around
   the net and republished in several languages.
   
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   Below is a note I just sent to the chairman of a physics department
   which requires freshmen to take a programming class offered by the
   engineering department. That course has traditionally taught Fortran
   77, but there is now a push on from the engineering school to switch
   to teaching C++. As usual, the arguments being mustered revolve around
   criticisms of Fortran 77 by people who are too out of touch to know
   much of anything about Fortran 90. In either case, I was asked by one
   of the senior physics faculty to contribute my two cents worth about
   what language they should be teaching. The appended note is the one I
   sent and it summarizes our corporate experience with C++ and some of
   our feelings about Fortran 90. Others may find this interesting since
   this question is arising throughout the academic community.
   
   John
   
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   Dr. John K. Prentice
   Quetzal Computational Associates
   3701 San Mateo N.E., Suite I
   Albuquerque, NM 87110-1249 USA
   Phone: 505-883-3706
   Email: john@quetzalcoatl.com
   
   
   My note to the Physics Department:
   
   One of the physics faculty members mentioned to me that the
   Engineering school is considering changing Eng 120 to teach C++
   instead of Fortran and that you were soliciting comments about this
   with regard to physics students. Even though I am not associated with
   the university, I wanted to contribute my thoughts, which come from a
   commercial as well as a research perspective. Perhaps they will be of
   some value to you.
   
   As you know, Quetzal Computational Associates specializes in
   computational science. We currently have projects computational
   physics, earth sciences, and agriculture for clients which include DoE
   and DoD laboratories as well as commercial clients as diverse as
   hazardous waste companies and grain companies. During the last year,
   we have developed numerical methods and codes based on them for
   modeling contaminant flow in porous media, modeling the structural
   mechanics of resonant sonic drilling rigs, modeling bistatic ground
   penetrator radar propagation in partially saturated soils, modeling
   solid dynamics at high strain rates, modeling the phenological
   development of corn and soy beans, modeling solar insolation in the
   photosynthetically active spectrum based on first principle
   atmospheric physics, and developed neural networks for the detection
   of dust clouds from satellite imagery.
   
   One of our largest computational physics projects is the development
   of advanced methods for modeling solid dynamics based on first
   principle physics. This is a multi-year, multi-million dollar project.
   This code numerically solves the partial differential equations for
   continuum solid dynamics using a hybrid finite volume/finite element
   technique, coupled to advanced equations of state and constitutive
   models for the solids and fluids in the calculation. This code and
   others we work with are huge number crunching codes, a modest 3d
   simulation will take 100 or more hours of Cray C-90 time to complete a
   single calculation. By any standard, they are amongst the largest
   computational physics simulations being done anywhere in the world. In
   addition, we are on the forefront in the application of parallel
   computing to these problems. We have projects to develop parallel
   versions of our codes for a diverse collection of computers, including
   networks of UNIX workstations, the IBM SP-2, the Cray T3D, and the
   Intel Paragon.
   
   For all of these projects, we employ Fortran 90 as our main language.
   We do some development work in Fortran 77, C, and C++, but we are
   moving away from those languages as quickly as possible. There are
   many reasons for our choice of Fortran 90, but first let me say a bit
   about why we are not enthusiastic about C++. The biggest strength of
   C++ is probably the availability of relatively inexpensive and high
   quality C++ compilers for PCs. But that is a pretty minor
   consideration in our business and it is outweighed by the enormous
   liabilities we have observed with C++. First, we regard C++ as the
   weakest of the object oriented languages. Objective C is a far more
   solid and well designed OOPS language, C++ is really some OOPS
   capability slapped on top of C. C++ is consequently extremely
   inefficient, inconsistent, overly large, and enormously difficult to
   program in. The experience of our clients mirrors our own, and in fact
   many DoE and DoD laboratories are finding that their headlong rush to
   C++ has been a hideously expensive mistake. I know of several C++
   scientific coding projects in the DoE that consumed millions of
   dollars and tens of man-years, only to be abandoned because the
   resulting code was enormously inefficient on both traditional serial
   computers and on their large parallel supercomputers. Similar horror
   stories abound throughout the programming community at this point.
   Bill Gates claimed that his biggest mistake in designing their new NT
   operating system was adopting C++ for the graphics coding, the
   resulting code took years longer to write than it should have and ran
   terribly slow. While OOPS is a solid development in the computer
   science community, I think it is fair to say that C++ is destined to
   be a passing fad, much like Pascal and Ada before it.
   
   The main reason C++ has attracted the attention it has in the
   scientific community is because Fortran 77 was a terribly outdated
   language. The many weaknesses of Fortran 77 were solved with Fortran
   90 however. Fortran 90 has every feature in C that is important to
   scientific programming and most of the features of an object oriented
   language (it lacks only inheritance and that is likely going to be
   added in Fortran 2000). However, unlike C and C++, Fortran 90 is
   designed to generate executable codes that are highly optimized and
   thus run extremely fast. An example is pointers. Pointers are integral
   to C and C++ programming and because the compiler cannot determine
   whether a pointer is aliased, it is impossible for it to determine
   interprocedural dependencies. The result is a significant degradation
   in optimization and extremely slow execution speeds (for most
   scientific codes, C and C++ generally produce code which is commonly
   an order of magnitude slower than Fortran 90 codes, based on the
   benchmarks we and others have done). Fortran 90 pointers are designed
   to give the functionality of pointers, but with restrictions that
   eliminate issues such as aliasing. From a programming perspective
   however, an even more important point is that Fortran 90 has more
   natural ways of expressing the functionality that C and C++ require
   pointers to express. Because of this, Fortran 90 is a more natural
   language to program in and the time required for debugging codes is a
   fraction of that required by C and C++ (C++ is much worse than C,
   provided you are really employing an OOPS paradigm, since you find
   yourself spending alot of debug time going up and down inheritance
   trees). Another important point is that the time required to learn
   Fortran 90 is much less than the time to learn either C or C++.
   
   Fortran 90 has another major advantage over C or C++. Modern
   scientific computing, and computing in general, is moving toward the
   use of parallel computers. Even PCs and workstations now come with
   multiple processors, so parallelism is something that everyone from an
   accountant to a physicist is encountering now. A major problem in
   programming parallel computers however is the linear memory model that
   is inherent to all procedural programming languages, with the singular
   exception of Fortran 90. A linear memory model is one that assumes
   that consecutive elements of an array are consecutive in memory. This
   was a reasonable assumption on traditional computers, but it is
   completely incorrect on a parallel computer. Only Fortran 90 has
   addressed this problem and providing standardized language support for
   parallelism. This support includes array syntax and many intrinsics
   for doing array operations varying from reduction operations such as
   array sums to matrix operations. With the use of Fortran 90 operator
   overloading and polymorphism, one can significantly extend the number
   of operations that avoid any reliance on the linear memory model. The
   fact that Fortran 90 moved away from a linear memory model is the main
   reason that it has become the base for so many data parallel languages
   such as Vienna Fortran, Fortran D, CRAFT, and High Performance
   Fortran. The availability of data parallel dialects of Fortran 90 is
   an especially large factor in favor of Fortran 90. Compilers for High
   Performance Fortran, for example, are now coming on the market for
   virtually every machine out there (including networks of workstations)
   and writing parallel codes in this language is straightforward. Of
   particular importance is that porting a Fortran 90 code to High
   Performance Fortran is extremely straightforward and codes written in
   High Performance Fortran can be run unaltered on a Fortran 90 compiler
   (with the exception of one HPF construct, the forall, which is being
   put into Fortran 95).
   
   My own opinion is that scientists today need to know more than one
   language or one computing paradigm. And I think it is entirely
   reasonable that students learn C++ before they graduate, though even
   more important is that they learn how to program MATLAB and a computer
   algebra system such as Maple or Macsyma. But the issue is what
   freshmen should learn as their first language and for that I would
   recommend Fortran 90 hands down. It is a better language for
   scientific programming and is both easier to learn and use than the
   alternatives. It is also much more likely to be the language students
   will be employing in their jobs upon graduation and it is the most
   promising route currently developing for the programming of parallel
   computers.