Distributed Computing
70 papers with code • 0 benchmarks • 1 datasets
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Libraries
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MNIST-8M
Most implemented papers
Energy-Efficient Edge-Facilitated Wireless Collaborative Computing using Map-Reduce
In this work, a heterogeneous set of wireless devices sharing a common access point collaborates to perform a set of tasks.
WONDER: Weighted one-shot distributed ridge regression in high dimensions
Here we study a fundamental and highly important problem in this area: How to do ridge regression in a distributed computing environment?
Distributed Voting in Beep Model
For the second algorithm, we show that it returns the correct output with high probability.
Hoplite: Efficient and Fault-Tolerant Collective Communication for Task-Based Distributed Systems
suquark/hoplite
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Task-based distributed frameworks (e. g., Ray, Dask, Hydro) have become increasingly popular for distributed applications that contain asynchronous and dynamic workloads, including asynchronous gradient descent, reinforcement learning, and model serving.
Communication-Efficient Distributed SVD via Local Power Iterations
As a practical surrogate of OPT, sign-fixing, which uses a diagonal matrix with $\pm 1$ entries as weights, has better computation complexity and stability in experiments.
Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based Methods
Recent advances in machine learning are consistently enabled by increasing amounts of computation.
MANGO: A Python Library for Parallel Hyperparameter Tuning
Tuning hyperparameters for machine learning algorithms is a tedious task, one that is typically done manually.
Accuracy-Efficiency Trade-Offs and Accountability in Distributed ML Systems
Trade-offs between accuracy and efficiency pervade law, public health, and other non-computing domains, which have developed policies to guide how to balance the two in conditions of uncertainty.
Communication-efficient distributed eigenspace estimation
Spectral methods are a collection of such problems, where solutions are orthonormal bases of the leading invariant subspace of an associated data matrix, which are only unique up to rotation and reflections.
Large-scale Neural Solvers for Partial Differential Equations
Photon-AI-Research/NeuralSolvers
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However, recent numerical solvers require manual discretization of the underlying equation as well as sophisticated, tailored code for distributed computing.
