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The LOCAL(A, B) randomized task scheduling algorithm is proposed for fully connected multiprocessors. It combines two given task scheduling algorithms ( A, and B) using local neighborhood search to give a hybrid of the two given algorithms. Objective is to show that such type of hybridization can give much better performance results in terms of parallel execution times. Two task scheduling algorithms are selected: DSC ( Dominant Sequence Clustering as algorithm A), and CPPS ( Cluster Pair Priority Scheduling as algorithm B) and a hybrid is created (the LOCAL(DSC, CPPS) or simply the LOCAL task scheduling algorithm). The LOCAL task scheduling algorithm has time complexity O(| V|| E|(| V |+| E|)), where V is the set of vertices, and E is the set of edges in the task graph. The LOCAL task scheduling algorithm is compared with six other algorithms: CPPS, DCCL ( Dynamic Computation Communication Load), DSC, EZ ( Edge Zeroing), LC ( Linear Clustering), and RDCC ( Randomized Dynamic Computation Communication). Performance evaluation of the LOCAL task scheduling algorithm shows that it gives up to 80.47 % improvement of NSL ( Normalized Schedule Length) over other algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a randomized scheduling algorithm on a fully connected homogeneous multiprocessor environment. This is a randomized version of our earlier algorithm in which we used priority of modules that was dependent on the computation and the communication times associated with the modules. First we propose a generalization of our earlier scheduling algorithm with restricted number of clusters to reduce the time complexity. Then we apply randomization to the generalized algorithm and demonstrate its superiority over our previous work. We show the complexity of our proposed algorithm as O(ab |V| (|V|+|E|) log (|V|+|E|)). Here a is the number of randomization steps, and b is a limit on the number of clusters formed. If we use a and b as constants, then this gives a better complexity in comparison with the complexity of our previous algorithm that was O(|V|2(|V|+|E|) log (|V|+|E|)). In comparison with our previous work we get a performance improvement of up to 6.63% and a performance improvement of up to 12.56% when compared with Sarkar's Edge Zeroing algorithm. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we give some extensive benchmark results for some dynamic priority clustering algorithms for homogeneous multiprocessor environments. By dynamic priority we mean a priority function that can change with every step of the algorithm. Using dynamic priority can give us more flexibility as compared to static priority algorithms. Our objective in this paper is to compare the dynamic priority algorithms with some well known algorithms from the literature and discuss their strengths and weaknesses. For our study we have selected two recently proposed dynamic priority algorithms: CPPS (Cluster Pair Priority Scheduling algorithm) having complexity and DCCL (Dynamic Computation Communication Load scheduling algorithm) having complexity where is the number of nodes in the task graph, and is the number of edges in the task graph. We have selected a recently proposed randomized algorithm with static priority (RCCL: Randomized Computation Communication Load scheduling algorithm) and converted it into a dynamic priority algorithm: RDCC (Randomized Dynamic Computation Communication load scheduling algorithm) having complexity where a is the number of randomization steps, and b is a limit on the number of clusters formed. We have also selected three well known algorithms from literature: DSC (Dominant Sequence Clustering algorithm) having complexity , EZ (Edge Zeroing algorithm) having complexity , and LC (Linear Clustering algorithm) having complexity . We have compared these algorithms using various comparison parameters including some statistical parameters, and also using various types of task graphs including some synthetic and real task graphs. Our results show that the dynamic priority algorithms give best results for the case of random task graphs, and for the case when the number of available processors are small. [Copyright &y& Elsevier]