A Generic Performance Model for Deep Learning in a Distributed Environment

TL;DR

This paper introduces a universal performance model for distributed deep learning applications that accurately predicts execution time by considering various intrinsic and extrinsic factors, applicable across different frameworks without code modifications.

Contribution

It presents a generic, adaptable performance model formulated as an optimization problem, validated on multiple frameworks, enhancing understanding and prediction of distributed deep learning performance.

Findings

Accurately predicts execution time across frameworks

Provides insights into performance and scalability factors

Does not require code instrumentation

Abstract

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.