31 citations · 73 across the 9 of their papers we have counts for
6 papers · 1 filter
With Great Freedom Comes Great Opportunity: Rethinking Resource Allocation for Serverless Functions
Muhammad Bilal, Marco Canini, Rodrigo Fonseca +1
Current serverless offerings give users a limited degree of flexibility for configuring the resources allocated to their function invocations by either coupling memory and CPU reso…
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…
Network Hardware-Accelerated Consensus
Huynh Tu Dang, Pietro Bressana, Han Wang +5
Consensus protocols are the foundation for building many fault-tolerant distributed systems and services. This paper posits that there are significant performance benefits to be ga…