Abstract: To improve the performance and efficiency of heterogeneous system， a two-stage task allocation model is proposed and implemented， by which the workload allocated to CPU and GPU is adjusted several times to decrease the execution time to the maximum extent. Firstly， the support vector machine (SVM) is used to classify a task into CPU and GPU in pre-treating. Then，after adjusting the allocation sets several times， the model carries out task allocation in the light of the characteristic and status of processors and the result produced by the first stage. Moreover， a real heterogeneous system is evaluated through several benchmarks on the proposed model. Experimental results demonstrate that the proposed model can achieve an average 43.54% of performance improvement， compared with some of the leading-edge allocation techniques.

Key words: GPU (graphics processing unit), SVM (support vector machine), heterogeneous system, machine learning, task pre-treat, task allocation