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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.