This dissertation has two complementary focuses. First, it provides a solution to large scale real-time system air traffic Control (ATC) using an enhanced SIMD machine model called an associative processor (AP). The second is the comparison of this implementation with a multiprocessor implementation and the implications of these comparisons. This paper demonstrates how one application, ATC, can more easily, more simply, and more efficiently be implemented on an AP than is generally possible on other types of traditional hardware. The AP implementation of ATC will take advantage of its deterministic hardware to use static scheduling. Our solution differs from previous ATC systems that are designed for MIMD computers and have a great deal of difficulty meeting the predictability requirements for ATC, which are critical for meeting the strict certification standards required for safety critical software components. The proposed AP solution supports accurate predictions of worst case execution times and guarantees all deadlines are met. Furthermore, the software developed based on the AP model is much simpler and smaller in size than the current corresponding ATC software. As the associative processor is built from SIMD hardware, it is considerably cheaper and simpler than the MIMD hardware currently used to support ATC. While APs were used for ATC-type applications earlier, these are no longer available. We use a ClearSpeed CSX600 accelerator to emulate the AP solutions of ATC on an ATC prototype consisting of eight data-intensive ATC real-time tasks. Its performance is evaluated in terms of execution time and predictability and is compared with an 8-core multiprocessor (MP) using OpenMP. Our extensive experiments show that the AP implementation meets all deadlines while the MP will regularly miss a large number of deadlines. It is shown that the proposed AP solution will support accurate predictions of worst case execution times and will guarantee that all deadlines are met. In addition, the AP code will be similar in size to sequential code for the same tasks and will avoid all of the additional support software needed with an MP to handle dynamic scheduling, load balancing, shared resource management, race conditions, and false sharing, etc. At this point, essentially only MIMD systems are built. Many of the advantages of using an AP to solve an ATC problem would carry over to other applications. AP solutions for a wide variety of applications will be cited in this paper. Applications that involve a high degree of data parallelism such as database management, text processing, image processing, graph processing, bioinformatics, weather modeling, managing UAS (Unmanned Aircraft Systems or drones) etc, are good candidates for AP solutions. This raises the issue of whether we should routinely consider using non-multiprocessor hardware like the AP for applications where substantially simpler software solutions will normally exist. It also raises the question of whether the use of both AP and MIMD hardware in the same system could provide more versatility and efficiency. Either the AP or MIMD could serve as the primary system but could hand off jobs it could not handle efficiently to the other system.