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Found 3 papers, 1 papers with code
Relaxed Scheduling for Scalable Belief Propagation
The ability to leverage large-scale hardware parallelism has been one of the key enablers of the accelerated recent progress in machine learning.
PopSGD: Decentralized Stochastic Gradient Descent in the Population Model
We prove that, under standard assumptions, SGD can converge even in this extremely loose, decentralized setting, for both convex and non-convex objectives.
A Concurrency-Optimal List-Based Set
Designing an efficient concurrent data structure is an important challenge that is not easy to meet.
Distributed, Parallel, and Cluster Computing
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