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Oral Session
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Mon 13:00 |
Distributed and Parallel Learning |
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Oral
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Mon 16:36 |
Hydrozoa: Dynamic Hybrid-Parallel DNN Training on Serverless Containers Runsheng Guo · Victor Guo · Antonio Kim · Josh Hildred · Khuzaima Daudjee |
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Workshop
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Thu 14:20 |
On Lower Bounds of Distributed Learning with Communication Compression Wotao Yin |
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Oral
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Tue 14:51 |
On the Utility of Gradient Compression in Distributed Training Systems Saurabh Agarwal · Hongyi Wang · Shivaram Venkataraman · Dimitris Papailiopoulos |
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Oral
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Tue 16:36 |
Synthesizing Optimal Parallelism Placement and Reduction Strategies on Hierarchical Systems for Deep Learning Ningning Xie · Tamara Norman · Dominik Grewe · Dimitrios Vytiniotis |
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Oral
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Mon 13:54 |
LightSecAgg: a Lightweight and Versatile Design for Secure Aggregation in Federated Learning Jinhyun So · Chaoyang He · Chien-Sheng Yang · Songze Li · Qian Yu · Ramy E. Ali · Basak Guler · Salman Avestimehr |
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Oral
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Tue 15:09 |
Efficient Strong Scaling Through Burst Parallel Training Seo Jin Park · Joshua Fried · Sunghyun Kim · Mohammad Alizadeh · Adam Belay |
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Oral
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Mon 13:18 |
Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs Hesham Mostafa |
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Tutorial
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Wed 13:00 |
ASTRA-sim: Enabling SW/HW Co-Design Exploration for Distributed Deep Learning Training Platforms Tushar Krishna |
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Oral
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Tue 9:03 |
VirtualFlow: Decoupling Deep Learning Models from the Underlying Hardware Andrew Or · Haoyu Zhang · Michael None Freedman |
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Oral
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Mon 8:45 |
Pathways: Asynchronous Distributed Dataflow for ML Sudip Roy · Jeff Dean · Sanjay Ghemawat · Ryan Sepassi · Hyeontaek Lim · Michael Isard · Paul Barham · Yonghui Wu · Laurent Shafey · Aakanksha Chowdhery · Chandu Thekkath · Brennan Saeta · Parker Schuh · Daniel Hurt · Ruoming Pang · Steven Hand |