Simulating Performance of ML Systems with Offline Profiling

Abstract

We advocate that simulation based on offline profiling is a promising approach to better understand and improve the complex ML systems. Our approach uses operation-level profiling and dataflow based simulation to ensure it offers a unified and automated solution for all frameworks and ML models, and is also accurate by considering the various parallelization strategies in a real system.

Accepted to The MLOps 2020 workshop, colocated with MLSys 2020. 2 pages

References in corpus (2)

• One weird trick for parallelizing convolutional neural networks
• Device Placement Optimization with Reinforcement Learning