Space-Time Adaptive Processing (STAP) refers to adaptive radar processing algorithms that take the signals from both multiple sensors and multiple pulses to cancel interferences and detect a target. Fully-adaptive STAP is known to be optimal, but the required number of operations is overwhelming, and makes this method impractical. Hence, many different heuristic approaches are sought to approximate the optimal method with smaller number of operations. In this work, we present a software framework called ALPS to help prototype various parallel STAP methods, and predict their performances.