Lune

MICRO2023Top-tier venue

Path Forward Beyond Simulators: Fast and Accurate GPU Execution Time Prediction for DNN Workloads

Ying Li, Yifan Sun, Adwait Jog

2023Year
16Citations
5Top-tier citations

Abstract

Today, DNNs’ high computational complexity and sub-optimal device utilization present a major roadblock to democratizing DNNs. To reduce the execution time and improve device utilization, researchers have been proposing new system design solutions, which require performance models (especially GPU models) to help them with pre-product concept validation. Currently, researchers have been utilizing simulators to predict execution time, which provides high flexibility and acceptable accuracy, but at the cost of a long simulation time. Simulators are becoming increasingly impractical to model today’s large-scale systems and DNNs, urging us to find alternative lightweight solutions.

Ask about this paper

Ask your agent about it.

Lune has read the top-tier papers around this one, so every answer names the papers it rests on.

Questions to start from

Your agent calls

Lunesearch_papers

Ask in Lune

Free to start. No credit card required.

lune papers get 1151edb5-1e5f-4db1-b459-b239d653ed36

Cited by top-tier papers5

Ask how each one uses it

Related papers

Dusk over the sea between two cliffs drawn in fine vertical lines