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Oral Session
Mon 13:00 Distributed and Parallel Learning
Mon 16:36 Hydrozoa: Dynamic Hybrid-Parallel DNN Training on Serverless Containers
Runsheng Guo · Victor Guo · Antonio Kim · Josh Hildred · Khuzaima Daudjee
Thu 14:20 On Lower Bounds of Distributed Learning with Communication Compression
Wotao Yin
Tue 14:51 On the Utility of Gradient Compression in Distributed Training Systems
Saurabh Agarwal · Hongyi Wang · Shivaram Venkataraman · Dimitris Papailiopoulos
Tue 16:36 Synthesizing Optimal Parallelism Placement and Reduction Strategies on Hierarchical Systems for Deep Learning
Ningning Xie · Tamara Norman · Dominik Grewe · Dimitrios Vytiniotis
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
Tue 15:09 Efficient Strong Scaling Through Burst Parallel Training
Seo Jin Park · Joshua Fried · Sunghyun Kim · Mohammad Alizadeh · Adam Belay
Mon 13:18 Sequential Aggregation and Rematerialization: Distributed Full-batch Training of Graph Neural Networks on Large Graphs
Hesham Mostafa
Wed 13:00 ASTRA-sim: Enabling SW/HW Co-Design Exploration for Distributed Deep Learning Training Platforms
Tushar Krishna
Tue 9:03 VirtualFlow: Decoupling Deep Learning Models from the Underlying Hardware
Andrew Or · Haoyu Zhang · Michael None Freedman
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