Modern Fortran Revived as the Language of Scientific Parallel Computing

The Fortran (FORmula TRANslating) computer language was the result of a project begun by John Backus at IBM in 1954. The goal of this project was to provide a way for programmers to express mathematical formulas through a formalism that computers could translate into machine instructions. Initially there was a great deal of skepticism about the efficacy of such a scheme. “How,’’ the scientists asked, “would anyone be able to tolerate the inefficiencies that would result from compiled code?’’ But, as it turned out, the first compilers were surprisingly good, and programmers were able, for the first time, to express mathematics in a high-level computer language. Fortran has evolved continually over the years in response to the needs of users, particularly in the areas of mathematical expressivity, program maintainability, hardware control (such as I/O), and, of course, code optimizations. In the meantime, other languages such as C and C11 have been designed to better meet the nonmathematical aspects of software design, such as graphical interfaces and complex logical layouts. These languages have caught on and have gradually begun to erode the scientific/engineering Fortran code base. By the 1980s, pronouncements of the “death of Fortran” prompted language designers to propose extensions to Fortran that would incorporate the best features of other high-level languages and, in addition, provide new levels of mathematical expressivity popular on supercomputers such as the CYBER 205 and the CRAY systems. This language became standardized as Fortran 90 (ISO/IEC 1539: 1991; ANSI X3.1981992). At the present time, Fortran 95, which includes many of the parallelization features of High Performance Fortran discussed later in this paper, is in the final stages of standardization. It is not yet clear whether the modernization of Fortran can, of itself, stem the C tide. However, I will demonstrate in this paper that modern Fortran is a viable mainstream language for parallelism. It is true that parallelism is not yet part of the scientific programming mainstream. However, it seems likely that, with the scientists’ never-ending thirst for affordable performance, parallelism will become much more common—especially