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6 June 2022 Characterizing ML training performance at the tactical edge
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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
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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
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KEYWORDS
Systems modeling

Data modeling

Performance modeling

Computer programming

Transformers

Computing systems

Profiling

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