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Portable Standard LISP (PSL) is a portable implementation of the programming language LISP constructed at the University of Utah. The version 3.4 of PSL was implemented for CRAY X-MP computers by Konrad-Zuse-Zentrum Berlin; this implementation is based to an important part on the earlier implementation of PSL 3.2 at the University of Utah, Los Alamos National Laboratories and CRAY Research Inc. at Mendota Heights. During the work on implementing PSL the language LISP was investigated for areas which can be supported by vector hardware. One area was found in the COMMON LISP sequence functions and some typical application areas of LISP programming can be improved by vector processing too. A model for the implementation of vector instructions in LISP was developed. For arithmetic an experimental vectorizing extent of the PSL compiler was constructed. With this means full vector hardware capacity can become available for LISP applications.
Portable Standard LISP (PSL) is a portable implementation of the programming language LISP constructed at the University of Utah. The version 3.4 of PSL was implemented for CRAY X-MP computers by Konrad Zuse-Zentrum Berlin; this implementation is based to an important part on the earlier implementation of PSL 3.2 at Salt Lake City, Los Alamos and Mendota Heights.
This guide describes the CRAY/COS REDUCE distribution tape and the procedures for installing, testing and maintaining REDUCE on a CRAY 1 or CRAY X-MP running COS. This document was derived from the corresponding document for Vax/Unix prepared by A. C. Hearn and L. R. Seward, The Rand Corporation, Santa Monica, publication CP84.
This document describes operating procedures for running REDUCE specific to the CRAY 1 and CRAY X-MP computers running the CRAY Operating System (COS). The document was derived from the corresponding document for Vax/UNIX prepared by A.C. Hearn and L.R. Seward, The Rand Corporation, Santa Monica, (CP85).
Portable Standard LISP (PSL), a dialect of LISP developed at the University of Utah, has been implemented and optimized for the CRAY 1 and CRAY X-MP supercomputers. This version uses a new implementation technique that permits a step-by-step development of the PSL kernel. The initial CRAY version was acceptable, although the execution speed of the PSL was not as fast as had been anticipated. CRAY-specific optimizations were undertaken that in some cases provided a ten-fold speed improvement, resulting in a fast LISP implementation.
This document describes operating procedures for running REDUCE specific to the CRAY 1 and CRAY X-MP computers running the Operating System UNICOS. The document was derived from the corresponding document for Vax/UNIX prepared by A. C. Hearn and L. R. Seward, The Rand Corporation, Santa Monica, (CP85).
This guide describes the CRAY/UNICOS REDUCE distribution tape and the procedures for installing, testing and maintaining REDUCE on a CRAY 1 or CRAY X-MP running UNICOS. This document was derived from the corresponding document for Vax/UNIX prepared by A. C. Hearn and L.R. Seward, The Rand Corporation, Santa Monica, publication CP84.
The Buchberger algorithm is a basic tool for the solution of systems of polynomial equations in an environment of computer algebra applications. A model for overlapped processing of different steps of the algorithm is presented, which uses the data structure of the polynomials (distributive representation) for synchronization. The model can be applied for multi processors with fast access to shared data. It is tested with Cray X-MP multi processors based on a parallel version of Portable Standard Lisp (PSL 3.4).
The paper presents a new application of computer algebra to the treatment of steady states of reaction systems. The method is based on the Buchberger algorithm. This algorithm was modified such that it can exploit the special structure of the equations derived from reaction systems, so even large systems can be handled. In contrast to numerical approximation techniques, the algebraic solution gives a complete and definite overview of the solution space and it is even applicable when parameter values are unknown or undetermined. The algorithm, its adaptation to the problem class and its application to selected examples are presented.