Characterizing ML training performance at the tactical edge

Abdulla Alshabanah; Keshav Balasubramanian; Bhaskar Krishnamachari; Murali Annavaram

doi:10.1117/12.2617773

6 June 2022 Characterizing ML training performance at the tactical edge

Abdulla Alshabanah, Keshav Balasubramanian, Bhaskar Krishnamachari, Murali Annavaram

Proceedings Volume 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV; 121131N (2022) https://doi.org/10.1117/12.2617773
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

The commercial industry has been working to develop ever-larger and more capable machine learning (ML) models (such as recent models from OpenAI, Microsoft and Google with more than ten billion parameters) for everything from general language processing to computer vision that have larger and larger computational, memory and other resource requirements. These powerful models generally need hardware accelerators to accommodate their workloads. GPU systems have been a popular choice among users and deep learning model designers either for their ability to run the inherently parallel deep learning workloads efficiently or for their large memory resource that would fit large deep learning models. We profile and analyze different deep learning workloads using various GPUs and configurations, emphasizing how deep learning architectures have diverse compute requirements. We analyze three popular deep learning workloads: Ultralytics’s You-Only-Look-Once model (Yolov5) on COCO dataset, Bidirectional Encoder Representations from Transformers (BERT), and Deep Learning Recommendation Model (DLRM) on the Criteo Kaggle Display Advertising Challenge Dataset. This work aims to shed light on the performance bottlenecks when using GPU systems as accelerators for training recent deep learning models.

Conference Presentation

Citation Download Citation

Abdulla Alshabanah, Keshav Balasubramanian, Bhaskar Krishnamachari, and Murali Annavaram "Characterizing ML training performance at the tactical edge", Proc. SPIE 12113, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV, 121131N (6 June 2022); https://doi.org/10.1117/12.2617773

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available