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Parallel computing systems compose task partitioning strategies in a true multiprocessing manner. Such systems share the algorithm and processing unit as computing resources which leads to highly inter process communications capabilities.We focus on real-time and non preemptive systems. A large variety of experiments have been conducted on the proposed algorithm. Goal of computation mo del is to provide a realistic representation of the costs of programming . The paper represents the optimal iterative task partitioning scheduling in the distributed heterogeneous environment. Main goal of the algorithm is to improve the performance of theschedule in the form of iteration using results from previous iterations. The algorithm first uses the b-level computation to calculate the initial schedule and then improve it iteratively.The results show the benefit of the task partitioning. The main characteristics of our method are optimal scheduling and strong link between partitioning, scheduling and communication. Some important models for task partitioning are also discussed in the paper. We target the algorithm for task partitioning which improvethe inter process communication between the tasks and use the recourses of the system in the efficient manner. The proposed algorithm contributes the inter-process communication cost minimization amongst the executing processes. This paper is the extendedversion of [15].

Optimal Task Partitioning Model in Distributed Heterogeneous Parallel Computing Environment