We discuss the results of a collaborative project on parallel processing of Synthetic Aperture Radar (SAR) data, carried out between the NASA/Jet Propulsion Laboratory (JPL), the California Institute of Technology (Caltech) and Intel Scalable Systems Division (SSD). Through this collaborative effort, we have successfully parallelized the most compute-intensive SAR correlator phase of the Spaceborne Shuttle Imaging Radar-C/X-Band SAR (SIR-C/X-SAR) code, for the Intel Paragon. We describe the data decomposition, the scalable high-performance I/O model, and the node-level optimizations which enable us to obtain efficient processing throughput. In particular, we point out an interesting double level of parallelization arising in the data decomposition which increases substantially our ability to support ''high volume'' SAR. Results are presented from this code running in parallel on the Intel Paragon. A representative set of SAR data, of size 800 Megabytes, which was collected by the SIR-C/X-SAR instrument aboard NASA's Space Shuttle in 15 seconds, is processed in 55 seconds on the Concurrent Supercomputing Consortium's Paragon XP/S 35+. This compares well with a time of 12 minutes for the current SIR-C/X-SAR processing system at JPL. For the first time, a commercial system can process SIR-C/X-SAR data at a rate which is approaching the rate at which the SIR-C/X-SAR instrument can collect the data. This work has successfully demonstrated the viability of the Intel Paragon supercomputer for processing ''high volume" Synthetic Aperture Radar data in near real-time.

We report on a project to develop a unified approach for building a library of collective communication operations that performs well on a cross-section of problems encountered in real applications. The target architecture is a two-dimensional mesh with worm-hole routing, but the techniques are more general. The approach differs from traditional library implementations in that we address the need for implementations that perform well for various sized vectors and grid dimensions, including non-power-of-two grids. We show how a general approach to hybrid algorithms yields performance across the entire range of vector lengths. Moreover, many scalable implementations of application libraries require collective communication within groups of nodes. Our approach yields the same kind of performance for group collective communication. Results from the Intel Paragon system are included.>

Task analysis procedures can be used in all stages of system development to examine the nature and severity of performance demands placed upon human operators. Task analyses involve considerable effort in obtaining a detailed set of measurements that represent the behavioural and cognitive tasks performed by the operators; these measures are referred to as task representation. The present article describes a flexible method for incorporating task representation data into an electronic spreadsheet. Such a computer-based procedure has many advantages over the more traditional paper and pencil approaches to task representation and analysis, including greater flexibility in data analysis and the inclusion of both objective and subjective measures of workload. Portions of an illustrative task representation and task analysis are presented along with a discussion of the strengths of computer-based task representation and analysis.