  A myriad of applications ranging from engineering and scientific simulations,
image and signal processing as well as high-sensitive data retrieval demand
high processing power reaching up to teraflops for their efficient execution.
While a standard serial computer would require clock-cycles of less than one
per second in this instance, parallel computing is a viable alternative. In
adopting parallelism, multiple architectural models such as the PRAM, BSP and
DataFlow Models have been proposed and implemented with some limitations due to
a number of factors. Perhaps one of the predominant causes is the presence of
sequential execution at some extent in these models. This status has trigged
the need for improved alternatives. Hence, the Arithmetic Deduction Model has
been introduced and its peculiarity can be seen through its use of natural
arithmetic concepts to perform computation, and the remarkable substitution or
elimination of dependency on variables and states in distributed data
processing. Although some initial results about its performance have been
published, it is critical to contextualize its genesis. Hence, in this paper we
explore the importance of high performance computing and conduct a comparative
study of some models of computation in terms of their strengh and limitations
and accordingly highlight the need for a new model of computation.
