LogGOPSC: A Parallel Computation Model Extending Network Contention into LogGOPS

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.