General Purpose Graphic Processing Unit (GPGPU) computing with CUDA has been effectively used in scientific applications, where huge accelerations have been achieved. However, while today’s traditional GPGPU can reduce the execution time of parallel code by many times, it comes at the expense of significant power and energy consumption. In this paper, we propose ubiquitous parallel computing approach for construction of decision tree on GPU. In our approach, we exploit parallelism of well-known ID3 algorithm for decision tree learning by two levels: at the outer level of building the tree node-by-node, and at the inner level of sorting data records within a single node. Thus, our approach not only accelerates the construction of decision tree via GPU computing, but also does so by taking care of the power and energy consumption of the GPU. Experiment results show that our approach outperforms purely GPU-based implementation and CPU-based sequential implementation by several times.