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05/03/2023
Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models
Sharding a large machine learning model across multiple devices to balan...
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09/02/2022
Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems
One of the key challenges of learning an online recommendation model is ...
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08/12/2022
AutoShard: Automated Embedding Table Sharding for Recommender Systems
Embedding learning is an important technique in deep recommendation mode...
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01/19/2022
Building a Performance Model for Deep Learning Recommendation Model Training on GPUs
We devise a performance model for GPU training of Deep Learning Recommen...
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05/04/2021
Alternate Model Growth and Pruning for Efficient Training of Recommendation Systems
Deep learning recommendation systems at scale have provided remarkable g...
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10/18/2020
Fast Distributed Training of Deep Neural Networks: Dynamic Communication Thresholding for Model and Data Parallelism
Data Parallelism (DP) and Model Parallelism (MP) are two common paradigm...
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10/16/2020
Adaptive Dense-to-Sparse Paradigm for Pruning Online Recommendation System with Non-Stationary Data
Large scale deep learning provides a tremendous opportunity to improve t...
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10/12/2017
On the Runtime-Efficacy Trade-off of Anomaly Detection Techniques for Real-Time Streaming Data
Ever growing volume and velocity of data coupled with decreasing attenti...
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08/07/2017