Distributed Computing
68 papers with code • 0 benchmarks • 1 datasets
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Libraries
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MNIST-8M
Most implemented papers
RankMap: A Platform-Aware Framework for Distributed Learning from Dense Datasets
This paper introduces RankMap, a platform-aware end-to-end framework for efficient execution of a broad class of iterative learning algorithms for massive and dense datasets.
Model Accuracy and Runtime Tradeoff in Distributed Deep Learning:A Systematic Study
This paper presents Rudra, a parameter server based distributed computing framework tuned for training large-scale deep neural networks.
Staleness-aware Async-SGD for Distributed Deep Learning
Deep neural networks have been shown to achieve state-of-the-art performance in several machine learning tasks.
Real-Time Community Detection in Large Social Networks on a Laptop
For a broad range of research, governmental and commercial applications it is important to understand the allegiances, communities and structure of key players in society.
Encoding Cryptographic Functions to SAT Using Transalg System
We implemented this technology in the form of the software system called Transalg, and used it to construct SAT encodings for a number of cryptanalysis problems.
Optimization for Large-Scale Machine Learning with Distributed Features and Observations
As the size of modern data sets exceeds the disk and memory capacities of a single computer, machine learning practitioners have resorted to parallel and distributed computing.
Easily parallelizable and distributable class of algorithms for structured sparsity, with optimal acceleration
From this unification we propose a continuum of preconditioned forward-backward operator splitting algorithms amenable to parallel and distributed computing.
A Security Monitoring Framework For Virtualization Based HEP Infrastructures
Based on this dataset, we will proceed to develop Machine Learning algorithms able to detect malicious Jobs.
Time Series Prediction for Graphs in Kernel and Dissimilarity Spaces
bpaassen/graph-edit-networks
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We propose to phrase time series prediction as a regression problem and apply dissimilarity- or kernel-based regression techniques, such as 1-nearest neighbor, kernel regression and Gaussian process regression, which can be applied to graphs via graph kernels.
Parallelizing Over Artificial Neural Network Training Runs with Multigrid
This work considers a multigrid reduction in time (MGRIT) algorithm that is able to parallelize over the thousands of training runs and converge to the exact same solution as traditional training would provide.
