LogGOPSC: A Parallel Computation Model Extending Network Contention into LogGOPS
Baicheng Yan, Yi Zhou, Xiao Limin, Jiantong Huo, Zhaokai Wang · 2019
Benefits from the simplicity, fastness and accuracy, the LogP model family is widely used to predict the parallel application communication performance, especial for the large-scale parallel application prediction or online prediction. However, this type of methods usually lacks consideration of modeling the network contention effect. This hinders their performance in predicting some real-world parallel applications. We thus propose a new parallel computation model via extending the LogGOPS mode with a parameter C. The additional parameter C is the additional time overhead caused by the network contention and it is predicted by a designed BP neural network. The experimental results show that LogGOPSC is more accurate than LogGOPS when there occur network contentions. Compared to the LogGOPS model, LogGOPSC gains a 93.25% average accuracy improvement for predicting 8mb point-to-point message passing. Furthermore, the average error of predicting two communication patterns is as low as 14.50% on the TianHe-2 HPC system.