A Generic Performance Model for Deep Learning in a Distributed
  Environment
Tulasi Kavarakuntla Liangxiu Han Huw Lloyd Annabel Latham Anthony Kleerekoper Samson B. Akintoye
Performance modelling of a deep learning application is essential to improve
and quantify the efficiency of the model framework. However, existing
performance models are mostly case-specific, with limited capability for the
new deep learning frameworks/applications. In this paper, we propose a generic
performance model of an application in a distributed environment with a generic
expression of the application execution time that considers the influence of
both intrinsic factors/operations (e.g. algorithmic parameters/internal
operations) and extrinsic scaling factors (e.g. the number of processors, data
chunks and batch size). We formulate it as a global optimization problem and
solve it using regularization on a cost function and differential evolution
algorithm to find the best-fit values of the constants in the generic
expression to match the experimentally determined computation time. We have
evaluated the proposed model on three deep learning frameworks (i.e.,
TensorFlow, MXnet, and Pytorch). The experimental results show that the
proposed model can provide accurate performance predictions and
interpretability. In addition, the proposed work can be applied to any
distributed deep neural network without instrumenting the code and provides
insight into the factors affecting performance and scalability.