908 citations · 2.4k across the 149 of their papers we have counts for
12 papers · 1 filter
Why Extension-Based Proofs Fail
Dan Alistarh, James Aspnes, Faith Ellen +2
We introduce extension-based proofs, a class of impossibility proofs that includes valency arguments. They are modelled as an interaction between a prover and a protocol. Using pro…
Distributed Learning over Unreliable Networks
Chen Yu, Hanlin Tang, Cedric Renggli +5
Most of today's distributed machine learning systems assume {\em reliable networks}: whenever two machines exchange information (e.g., gradients or models), the network should guar…
The Convergence of Sparsified Gradient Methods
Dan Alistarh, Torsten Hoefler, Mikael Johansson +3
Distributed training of massive machine learning models, in particular deep neural networks, via Stochastic Gradient Descent (SGD) is becoming commonplace. Several families of comm…
Relaxed Schedulers Can Efficiently Parallelize Iterative Algorithms
Dan Alistarh, Trevor Brown, Justin Kopinsky +1
There has been significant progress in understanding the parallelism inherent to iterative sequential algorithms: for many classic algorithms, the depth of the dependence structure…
The Transactional Conflict Problem
Dan Alistarh, Syed Kamran Haider, Raphael Kübler +1
The transactional conflict problem arises in transactional systems whenever two or more concurrent transactions clash on a data item. While the standard solution to such conflicts…
Distributionally Linearizable Data Structures
Dan Alistarh, Trevor Brown, Justin Kopinsky +2
Relaxed concurrent data structures have become increasingly popular, due to their scalability in graph processing and machine learning applications. Despite considerable interest,…