In this paper, we review and compare the methods of parallel applications’ development based on the automatic program parallelizing for computer systems with shared and distributed memory and on the information graph’s hardware costs and performance reduction for reconfigurable computer systems. The increase in the number of computer system’s units or in the problem’s dimension leads to the significant growth of the automatic parallelization complexity for a procedural program. As a result, the obtainment of parallelizing results in acceptable time using state-of-the-art computer systems is very problematic. In reconfigurable computer systems, the reduction of absolutely parallel information graph of a problem is applied for the parallel program creation. The information graph illustrates the parallelizing and pipelining of computations. In addition to the traditionally practiced reduction of basic subgraphs’ number, the reductions of computational operations’ quantity and of data digit capacity can be utilized for the performance or hardware costs’ scaling. We have proved that the methods of information graph hardware costs and performance reduction provide a considerable decrease in the number of steps needed for adaptation of parallel application to reconfigurable computer systems’ architectures in comparison with automatic parallelizing. We have proved the theorem of coefficient value at sequential reduction, the theorem of increase in reduction coefficient at custom value and the theorem of commutativity of various reduction transformations. The proved theorems help to find a rational sequence of reduction transformations.
Parallel Program Debugging with MAD — A Practical Approach