Classification is an important issue both in Machine Learning and Data Mining. Decision tree is one of the famous classification models. In the reality case, the dimension of data is high and the data size is huge. Building a decision in large data base cost much time in computation. It is a computationally expensive problem. GPU is a special design processor of graphic. The highly parallel features of graphic processing made today’s GPU architecture. GPGPU means use GPU to solve non-graphic problems which need amounts of computation power. Since the high performance and capacity/price ratio, many researches use GPU to process lots computation. Compute Unified Device Architecture (CUDA) is a GPGPU solution provided by NVIDIA. This paper provides a new parallel decision tree algorithm base on CUDA. The algorithm parallel computes building phase of decision tree. In our system, CPU is responsible for flow control and GPU is responsible for computation. We compare our system to the Weka-j48 algorithm. The result shows out system is 6~5x times faster than Weka-j48. Compare with SPRINT on large data set, our CUDT has about 18 times speedup. 關鍵詞:GPGPU、CUDA、Decision Tree、Classification
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