A thread partition approach based on BP neural network

Abstract

Thread-Level Speculation (TLS) is a thread-level automatic parallelization technique to accelerate sequential programs on multi-core. Thread partition is a core step for this technique, so how to automatically and effectively partition an unknown program is a key to improve the efficiency of this technique. In order to solve this problem, this paper proposes a Back Propagation Neural Network based threading partition approach(TPoBP). This approach is used to study the implicit knowledge of partition in the sample set to guide the partition for unknown programs. The knowledge in the sample is composed of the characteristics of the sample and the partition scheme, which are used as the input and output of the network to train the network until the specified accuracy is reached. During validation period, the trained network makes use of profiling information (obtained during pre-execution) of a validation program as input, and runs to obtain the predicted partition scheme for the validation program. Experimental results show that TPoBP can effectively predict the partition schemes of validation programs, and average prediction accuracy almost reaches 0.7. Moreover, these predicted schemes are further used to guide partition for validation programs, and Olden benchmarks reach a maximum 11.8% speedup improvement. Experiments demonstrate that the model proposed by this paper is effective to predict partition scheme for unseen programs.