Adaptive pre-task assignment scheduling strategy for heterogeneous distributed raytracing system

One of the main obstacles in obtaining high performance from heterogeneous distributed computing (HDC) system is the inevitable communication overhead. This occurs when tasks executing on different computing nodes exchange data or the assigned sub-task size is very small. In this paper, we present adaptive pre-task assignment (APA) strategy for heterogeneous distributed raytracing system. In this strategy, the master assigns pre-task to the each node. The size of sub-task for each node is proportional to the node's performance. One of the main features of this strategy is that it reduces the inter-processes communication, the cost overhead of the node's idle time and load imbalance, which normally occurs in traditional runtime task scheduling (RTS) strategies. Performances of the RTS and APA strategies are evaluated on manager/master and workers model of HDC system. The experimental results of our proposed (APA) strategy have shown a significant improvement in the performance over RTS strategy. Keywords: Task partitioning and scheduling: Load balancing: Heterogeneous distributed computing; Runtime task scheduling strategy; Adaptive pre-task assignment strategy; Distributed image/raytracing computing; Performance evaluation