Sensitivity derivative (SD) calculation via automatic differentiation typical of that required for the aerodynamic design of a transport-type aircraft is considered. Two ways of computing SDs via code generated by the ADIFOR automatic differentiation tool are compared for efficiency and applicability to problems involving large numbers of design variables. A vector implementation on a CRAY Y-MP computer is compared with a coarse-grained parallel implementation on an IBM SP1 computer, employing a Fortran M wrapper. The SDs are computed for a swept transport wing in turbulent, transonic flow; the number of geometric design variables varies from 1 to 60, with coupling between a wing grid generation program and a state-of-the-art, 3-D computational fluid dynamics program, both augmented for derivative computation via AD. For a small number of design variables, the CRAY Y-MP implementation is much faster. As the number of design variables grows, however, the SP1 becomes an attractive alternative in terms of compute speed, job turnaround time, and total memory available for solutions with large numbers of design variables. The coarse-grained parallel implementation also can be moved easily to a network of workstations.

One of the key components of a multi-user multimedia-on-demand system is the data server. Digitalization of traditionally analog data such as video and audio, and the feasibility of obtaining network bandwidths above the gigabit-per-second range are two important advances that have made possible the realization, in the near future, of interactive distributed multimedia systems. Secondary-to-main memory I/O technology has not kept pace with advances in networking, main memory and CPU processing power. Consequently, the performance of the server has a direct bearing on the overall performance of such a system. In this paper we present a high-performance solution to the I/O retrieval problem in a distributed multimedia system. We develop a model for the architecture of a server for such a system. Parallelism of data retrieval is achieved by striping the data across multiple disks. We present the algorithms for server operation when servicing a constant number of streams, as well as the admission control policy for accepting requests for new streams. The performance of any server ultimately depends on the data access patterns. Two modifications of the basic retrieval algorithm are presented to exploit data access patterns in order to improve system throughput and response time. Finally, we present preliminary performance results of these algorithms on the IBM SP1 and Intel Paragon parallel computers.

This paper presents an evaluation of two distributed-memory operating systems, Paragon OSF/1 and SUNMOS, run on the 227 node Intel Paragon XP/S-15 installed at the Numerical Aerodynamic Simulation (NAS) facility. Paragon OSF/1 is based on the Open Software Foundation Advanced Development (OSF/1 AD) microkernel. SUNMOS is a custom microkernel developed by Sandia National Laboratory and the University of New Mexico. Paragon OSF/1's design goals are to maintain the full OSF/1 functionality on each node. SUNMOS' goals are performance and scalability, trading functionality to deliver performance. The paper compares the functionality and performance of four operating system services essential to the efficient execution of a parallel application. These services are: kernel inter-process communication (IPC), memory management, application message -passing, and disk I/O throughput. The NAS Parallel Benchmarks (NPB) were also used to compare overall performance. Our evaluation shows that signifi...

This paper presents an evaluation of two distributed-memory operating systems, Paragon OSF/1 and SUNMOS, run on the 227 node Intel Paragon XP/S-15 installed at the Numerical Aerodynamic Simulation (NAS) facility. Paragon OSF/1 is based on the Open Software Foundation Advanced Development (OSF/1 AD) microkernel. SUNMOS is a custom microkernel developed by Sandia National Laboratory and the University of New Mexico. Paragon OSF/1's design goals are to maintain the full OSF/1 functionality on each node. SUNMOS' goals are performance and scalability, trading functionality to deliver performance. The paper compares the functionality and performance of four operating system services essential to the efficient execution of a parallel application. These services are: kernel inter-process communication (IPC), memory management, application message -passing, and disk I/O throughput. The NAS Parallel Benchmarks (NPB) were also used to compare overall performance. Our evaluation shows ...