The remote sensing techniques have put great pressure on real-time waveform post-processing design. Due to the intensive computation and multi-channel waveform integration, the overhead between the processing time and the storage of amount of data prior to downlink issues has lead us to get the solution of task-level parallelism. With the development of IC design and innovation of architecture, embedded system can range from a single microprocessor to a complex multi-processor and even including the embedded operating system (OS) on a chip. Therefore symmetric multiprocessing (SMP) with embedded OS offers an attractive way to expose coarse-grained parallelism application. In this paper we demonstrate a new modeling approach. In order to simplify the system; a workload model is derived from a remote sensing application, which represents the workload characteristic and time degrading factors. The intention is to leverage the task-level parallelism load is evenly to each processor in SMP, with the OS level testing to speculate the bottleneck in hardware level. This parallel workload model which maps to a 6-LEON3 SMP architecture, attains a 2.7x mean speedup over a single-LEON3 baseline; with 3-LEON3 attains a 2.23x mean speedup; with 2- LEON3 attains a 1.78x mean speedup over a single-LEON3 baseline. Due to the involved sharing resources and scheduling of multiple CPUs, the system will have a degrading in processing speed. With this lag we could infer the hardware pipeline efficiency. And afford on the processor-set subsystem and memory subsystem analysis reveal the affects on the system throughput.