117 citations · 411 across the 25 of their papers we have counts for
4 papers · 2 filters
On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning
Aritra Dutta, El Houcine Bergou, Ahmed M. Abdelmoniem +4
Compressed communication, in the form of sparsification or quantization of stochastic gradients, is employed to reduce communication costs in distributed data-parallel training of…
Assise: Performance and Availability via NVM Colocation in a Distributed File System
Thomas E. Anderson, Marco Canini, Jongyul Kim +6
The adoption of very low latency persistent memory modules (PMMs) upends the long-established model of disaggregated file system access. Instead, by colocating computation and PMM…
Scaling Distributed Machine Learning with In-Network Aggregation
Amedeo Sapio, Marco Canini, Chen-Yu Ho +7
Training machine learning models in parallel is an increasingly important workload. We accelerate distributed parallel training by designing a communication primitive that uses a p…
Partitioned Paxos via the Network Data Plane
Huynh Tu Dang, Pietro Bressana, Han Wang +6
Consensus protocols are the foundation for building fault-tolerant, distributed systems, and services. They are also widely acknowledged as performance bottlenecks. Several recent…