In recent years a lot of attention has been focused on writing error-free programs in an easily readable and understandable manner. It is also recognized that the so called von Neumann" or Imperative" languages may not be the right medium to work in this direction as these have a complex body lacking the solid foundation of computational mathematics. With the announcement of yet another such language, there is a further addition of various new language constructs thereby only helping to build up the confusion. On the other hand, functional languages are based upon a solid foundation and produce programs which are semantically very clear. These language, however, have not found favor with the computing community primarily because these are not history sensitive apart from being inefficient to run on presently available computers. An appearling alternative has been proposed by Backus in terms of an applicative language independent of the lambda calculus and possessing history sensitivity by means of a loose coupling between computation and the state (of the store). In this paper, we pick up his ideas and work up a computation scheme which introduces an amount of abstraction in the representation of variables. Specifically, we do not bind a variable to a particular value to the declared type, but rather we assign limits to the values of the variable. These limits are changeable and depend upon the available semantic information. It is observed that such a scheme can exploit the potentials of working at higher levels, notable among which in this particular scheme is, the possibility of considerable increase in the speed of computation.

In order to reduce the number of bus conflicts, a multiple bus connection and clustering of multiprocessor system have been proposed. In this paper throughputs of these two types of multiprocessor systems are investigated numerically in some detail.

A parallel sorting is actually executed on the multiprocessor system to estimate the real performance. The sorting algorithm consists of merging and exchanging data between the nearest neighbors. The system performance of parallel sorting is measured experimentally. It is proved that the parallel sorting algorithm is really effective for a multiprocessor system with the limited number of processors.

A new parallel sorting algorithm is proposed, assuming a linearly connected multiprocessor system with a limited number of processors. The algorithm based on merging and exchanging data between adjacent processors. The upper bound is analytically investigated, and the throughput is proportional to the number of processors. The time complexity is O(N) for N data with O(log N) processors. Average number of iterations for a multiprocessor with two processing elements is also analytically investigated. Execution time for real situation is numerically simulated on a conventional computer.