3.2 Which language to optimize

It has been said, “I don’t know what language they will be using to program high performance computers 10 years from now, but we do know it will be called FORTRAN.” At the risk of inciting outright warfare, we need to discuss the strengths and weaknesses of languages that are used for high performance computing. Most computer scientists (not computational scientists) train on a steady diet of C, C++, Just for the record, both the authors of this book are quite accomplished in C, C++, and FORTRAN, so they have no preconceived notions. or some other language focused on data structures or objects. When students encounter high performance computing for the first time, there is an immediate desire to keep programming in their favorite language. However, to get the peak performance across a wide range of architectures, FORTRAN is the only practical language.

When students ask why this is, usually the first answer is, “Because it has always been that way.” In one way this is correct. Physicists, mechanical engineers, chemists, structural engineers, and meteorologists do most programming on high performance computers. FORTRAN is the language of those fields. (When was the last time a computer science student wrote a properly working program that computed for a week?) So naturally the high performance computer vendors put more effort into making FORTRAN work well on their architecture.

This is not the only reason that FORTRAN is a better language, however. There are some fundamental elements that make C, C++, or any data structures-oriented language unsuitable for high performance programming. In a word, that problem is pointers . Pointers (or addresses) are the way good computer scientists construct linked lists, binary trees, binomial queues, and all those nifty data structures. The problem with pointers is that the effect of a pointer operation is known only at execution time when the value of the pointer is loaded from memory. Once an optimizing compiler sees a pointer, all bets are off. It cannot make any assumptions about the effect of a pointer operation at compile time. It must generate conservative (less optimized) code that simply does exactly the same operation in machine code that the high-level language described.

While the lack of pointers in FORTRAN is a boon to optimization, it seriously limits the programmer’s ability to create data structures. In some applications, especially highly scalable network-based applications, the use of good data structures can significantly improve the overall performance of the application. To solve this, in the FORTRAN 90 specification, pointers have been added to FORTRAN. In some ways, this was an attempt by the FORTRAN community to keep programmers from beginning to use C in their applications for the data structure areas of their applications. If programmers begin to use pointers throughout their codes, their FORTRAN programs will suffer from the same problems that inhibit optimization in C programs. In a sense FORTRAN has given up its primary advantage over C by trying to be more like C. The debate over pointers is one reason that the adoption rate of FORTRAN 90 somewhat slowed. Many programmers prefer to do their data structure, communications, and other bookkeeping work in C, while doing the computations in FORTRAN 77.

FORTRAN 90 also has strengths and weaknesses when compared to FORTRAN 77 on high performance computing platforms. FORTRAN 90 has a strong advantage over FORTRAN 77 in the area of improved semantics that enable more opportunities for advanced optimizations. This advantage is especially true on distributed memory systems on which data decomposition is a significant factor. (See [link] .) However, until FORTRAN 90 becomes popular, vendors won’t be motivated to squeeze the last bit of performance out of FORTRAN 90.

So while FORTRAN 77 continues to be the mainstream language for high performance computing for the near future, other languages, like C and FORTRAN 90, have their limited and potentially increasing roles to play. In some ways the strongest potential challenger to FORTRAN in the long run may come in the form of a numerical tool set such as Matlab. However, packages such as Matlab have their own set of optimization challenges that must be overcome before they topple FORTRAN 77’s domination.