Analysis of large deviations behavior of multi-GPU memory access in deep learning

The unpredictable nature of irregular memory accesses in a mixed memory applications such as deep learning application poses many challenges due to the communication issues. Typically, a multi-GPU node that has a large number of simultaneous memory requests consumes almost 80% of the processing time for memory mapping. This calls for characterization of mixed regular and irregular memory accesses so that memory divergence can be simplified to improve performance. In this paper, using large deviations principle, it is shown that the mixed regular and irregular memory accesses can be viewed as a combination of continuous and discrete functions. This view point is proved to give better performance through characterization of memory divergence in multi-GPU node using the sub-additivity property. Further, a detection test procedure based on quenched large deviations model is proposed which generates threshold values for optimizing the memory mapping in data intensive applications and hence it will improve the performance.