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With the NoC size growing constantly, efficient algorithms are needed to provide power/performance-aware task mapping on massively parallel systems. In this paper a novel tree-model based mapping algorithm is proposed, to achieve high energy efficiency and low latency on NoC platforms. A NoC is abstracted as a tree composed of a root node and median nodes at different levels. By mapping tasks starting from the root of the tree, our algorithm minimizes the communication cost and consequently reduces the energy consumption and network delay. Experimental results show that the run-time of our algorithm is decreased by 90% on average compared to the Greedy Incremental (GI) algorithm. Full system simulation also shows that for Radix traffic, compared to the original random mapping, the GI achieves 18.7% and 17.3% reduction in energy consumption and average network latency respectively, while our algorithm achieves 24.7% and 40.8% reduction respectively.