No abstract

The recent success of vector computers such as the Cray-1 and array processors such as those manufactured by Floating Point Systems has increased interest in making vector operations available to the FORTRAN programmer. The FORTRAN standards committee is currently considering a successor to FORTRAN 77, usually called FORTRAN 8x, that will permit the programmer to explicitly specify vector and array operations.Although FORTRAN 8x will make it convenient to specify explicit vector operations in new programs, it does little for existing code. In order to benefit from the power of vector hardware, existing programs will need to be rewritten in some language (presumably FORTRAN 8x) that permits the explicit specification of vector operations. One way to avoid a massive manual recoding effort is to provide a translator that discovers the parallelism implicit in a FORTRAN program and automatically rewrites that program in FORTRAN 8x.Such a translation from FORTRAN to FORTRAN 8x is not straightforward because FORTRAN DO loops are not always semantically equivalent to the corresponding FORTRAN 8x parallel operation. The semantic difference between these two constructs is precisely captured by the concept of dependence. A translation from FORTRAN to FORTRAN 8x preserves the semantics of the original program if it preserves the dependences in that program.The theoretical background is developed here for employing data dependence to convert FOR-TRAN programs to parallel form. Dependence is defined and characterized in terms of the conditions that give rise to it; accurate tests to determine dependence are presented; and transformations that use dependence to uncover additional parallelism are discussed.

No abstract

No abstract

Advances in networking technologies will soon make it possible to use the global information infrastructure in a qualitatively different way—as a computational as well as an information resource. As described in the recent book The Grid: Blueprint for a New Computing Infrastructure, this Grid will connect the nation’s computers, databases, instruments, and people in a seamless web of computing and distributed intelligence, which can be used in an on-demand fashion as a problem-solving resource in many fields of human endeavor—and, in particular, science and engineering. The availability of grid resources will give rise to dramatically new classes of applications, in which computing resources are no longer localized but, rather, distributed, heterogeneous, and dynamic; computation is increasingly sophisticated and multidisciplinary; and computation is integrated into our daily lives and, hence, subject to stricter time constraints than at present. The impact of these new applications will be pervasive, ranging from new systems for scientific inquiry, through computing support for crisis management, to the use of ambient computing to enhance personal mobile computing environments. To realize this vision, significant scientific and technical obstacles must be overcome. Principal among these is usability. The goal of the Grid Application Development Software (GrADS) project is to simplify distributed heterogeneous computing in the same way that the World Wide Web simplified information sharing over the Internet. To that end, the project is exploring the scientific and technical problems that must be solved to make it easier for ordinary scientific users to develop, execute, and tune applications on the Grid. In this paper, the authors describe the vision and strategies underlying the GrADS project, including the base software architecture for grid execution and performance monitoring, strategies and tools for construction of applications from libraries of grid-aware components, and development of innovative new science and engineering applications that can exploit these new technologies to run effectively in grid environments.