Within NASA's High Performance Computing and Communication (HPCC) program, the NASA Glenn Research Center (GRC) is developing a large scale, detailed simulation environment for the analysis and design of aircraft engines called the Numerical Propulsion System Simulation (NPSS). The three major aspects of modeling capabilities focused in NPSS, including integration of different engine components, coupling of multiple disciplines, and engine component zooming at appropriate level of fidelity, require relatively tight coupling of different analysis codes. Most of these codes in aerodynamics and solid mechanics are written in Fortran. Refitting these legacy Fortran codes with distributed objects can increase the code reusability. We describe our experiences in building a CORBA-based development environment for programmers to easily wrap and couple legacy Fortran codes. This environment consists of a C++ wrapper library to hide the details of CORBA and an efficient remote variable scheme to facilitate data exchange between the client and the server. We also report empirical performance evaluation results and describe current applications.

A distributed object is a reusable, self-contained piece of software that cooperates with other objects on the same machine or across a network in a plug-and-play fashion via a well-defined interface. The Numerical Propulsion System Simulation (NPSS) attempts to provide a collaborative aircraft engine design and simulation environment based on this concept. Many scientific applications in aerodynamics and solid mechanics are written in Fortran. Refitting these legacy Fortran codes with distributed objects can increase the code reusability. The remote variable scheme provided in NPSS helps programmers easily migrate the Fortran codes towards a client-server platform. This scheme gives the client the capability of accessing the variables at the server site. Through the operator overloading features in C++, remote variables can be used in much the same way as traditional variables. The remote variable scheme adopts the lazy update approach and the prefetch method. Preliminary performance evaluation shows that communication overhead can be greatly reduced.

Abstract A distributed object is a reusable, self‐contained piece of software that cooperates with other objects on the same machine or across a network in a plug‐and‐play fashion via a well‐defined interface. The Numerical Propulsion System Simulation (NPSS) attempts to provide a collaborative aircraft engine design and simulation environment based on this concept. Many scientific applications in aerodynamics and solid mechanics are written in Fortran. Refitting this legacy Fortran code with distributed objects can increase code reusability. In this paper, we focus on the novel use of a remote variable scheme to help programmers migrate the Fortran code towards a client–server architecture. This scheme gives the client the capability of accessing variables at the server site and makes it easier for programmers to couple component engine code. Through the operator overloading features in C++, remote variables can be used in much the same way as traditional variables. The remote variable scheme adopts the lazy update approach and the prefetch method. Preliminary performance evaluation shows that communication overhead can be greatly reduced. Copyright © 2001 John Wiley & Sons, Ltd.

An efficient run time, statistical scheme for estimating the execution time of a task is presented, in order to facilitate run time matching and scheduling in a distributed heterogeneous computing environment. This scheme is based upon a nonparametric regression technique, where the execution time estimate for a task is computed from past observations. Furthermore, this technique is able to compensate for different parameters upon which the execution time depends, and does not require any knowledge of the architecture of the target machine. It is also able to make accurate predictions when erroneous data is present in the set of observations, and has been experimentally shown to produce estimates with very low error even with few past values from which to calculate a new estimate.