Papers Distributed Computing
“Distributed Computing” 태그가 달린 논문 379편 · 필터 해제
Solving Billion-Scale Knapsack Problems
Knapsack problems (KPs) are common in industry, but solving KPs is known to be NP-hard and has been tractable only at a relatively small scale. This paper examines KPs in a slightly generalized form and shows that they c…
Distributed ComputingA Robust Real-Time Computing-based Environment Sensing System for Intelligent Vehicle
For intelligent vehicles, sensing the 3D environment is the first but crucial step. In this paper, we build a real-time advanced driver assistance system based on a low-power mobile platform. The system is a real-time mu…
BIG-bench Machine LearningDistributed ComputingGPUStereo MatchingFast-Fourier-Forecasting Resource Utilisation in Distributed Systems
Distributed computing systems often consist of hundreds of nodes, executing tasks with different resource requirements. Efficient resource provisioning and task scheduling in such systems are non-trivial and require clos…
Distributed ComputingSchedulingUnifying Graph Convolutional Networks as Matrix Factorization
In recent years, substantial progress has been made on graph convolutional networks (GCN). In this paper, for the first time, we theoretically analyze the connections between GCN and matrix factorization (MF), and unify …
Distributed ComputingNode ClassificationOne-shot Distributed Ridge Regression in High Dimensions
To scale up data analysis, distributed and parallel computing approaches are increasingly needed. Here we study a fundamental problem in this area: How to do ridge regression in a distributed computing environment? We st…
Distributed ComputingregressionUnityVocal Bursts Intensity PredictionISLET: Fast and Optimal Low-rank Tensor Regression via Importance Sketching
In this paper, we develop a novel procedure for low-rank tensor regression, namely \emph{\underline{I}mportance \underline{S}ketching \underline{L}ow-rank \underline{E}stimation for \underline{T}ensors} (ISLET). The cent…
Distributed ComputingregressionCommunication-Efficient Local Decentralized SGD Methods
Recently, the technique of local updates is a powerful tool in centralized settings to improve communication efficiency via periodical communication. For decentralized settings, it is still unclear how to efficiently com…
Distributed ComputingGuardians of the Deep Fog: Failure-Resilient DNN Inference from Edge to Cloud
Partitioning and distributing deep neural networks (DNNs) over physical nodes such as edge, fog, or cloud nodes, could enhance sensor fusion, and reduce bandwidth and inference latency. However, when a DNN is distributed…
Distributed ComputingSensor FusionConstrained Multi-Objective Optimization for Automated Machine Learning
Automated machine learning has gained a lot of attention recently. Building and selecting the right machine learning models is often a multi-objective optimization problem. General purpose machine learning software that …
BIG-bench Machine LearningDistributed ComputingResolvable Designs for Speeding up Distributed Computing
Distributed computing frameworks such as MapReduce are often used to process large computational jobs. They operate by partitioning each job into smaller tasks executed on different servers. The servers also need to exch…
Distributed ComputingMulti-owner Secure Encrypted Search Using Searching Adversarial Networks
Searchable symmetric encryption (SSE) for multi-owner model draws much attention as it enables data users to perform searches over encrypted cloud data outsourced by data owners. However, implementing secure and precise …
Distributed ComputingDistributed Deep Convolutional Neural Networks for the Internet-of-Things
Severe constraints on memory and computation characterizing the Internet-of-Things (IoT) units may prevent the execution of Deep Learning (DL)-based solutions, which typically demand large memory and high processing load…
Decision MakingDistributed ComputingVideo ClassificationLearning over inherently distributed data
The recent decades have seen a surge of interests in distributed computing. Existing work focus primarily on either distributed computing platforms, data query tools, or, algorithms to divide big data and conquer at indi…
Distributed ComputingTaming Momentum in a Distributed Asynchronous Environment
Although distributed computing can significantly reduce the training time of deep neural networks, scaling the training process while maintaining high efficiency and final accuracy is challenging. Distributed asynchronou…
16kDistributed ComputingOptuna: A Next-generation Hyperparameter Optimization Framework
The purpose of this study is to introduce new design-criteria for next-generation hyperparameter optimization software. The criteria we propose include (1) define-by-run API that allows users to construct the parameter s…
Distributed ComputingHyperparameter OptimizationASYNC: A Cloud Engine with Asynchrony and History for Distributed Machine Learning
ASYNC is a framework that supports the implementation of asynchrony and history for optimization methods on distributed computing platforms. The popularity of asynchronous optimization methods has increased in distribute…
BIG-bench Machine LearningDistributed ComputingWhat graph neural networks cannot learn: depth vs width
This paper studies the expressive power of graph neural networks falling within the message-passing framework (GNNmp). Two results are presented. First, GNNmp are shown to be Turing universal under sufficient conditions …
Distributed ComputingMining Association Rules in Various Computing Environments: A Survey
Association Rule Mining (ARM) is one of the well know and most researched technique of data mining. There are so many ARM algorithms have been designed that their counting is a large number. In this paper we have surveye…
Cloud ComputingDistributed ComputingSurveyDeploying AI Frameworks on Secure HPC Systems with Containers
The increasing interest in the usage of Artificial Intelligence techniques (AI) from the research community and industry to tackle "real world" problems, requires High Performance Computing (HPC) resources to efficiently…
Distributed ComputingOnline Collection and Forecasting of Resource Utilization in Large-Scale Distributed Systems
Large-scale distributed computing systems often contain thousands of distributed nodes (machines). Monitoring the conditions of these nodes is important for system management purposes, which, however, can be extremely re…
Anomaly DetectionDistributed ComputingManagementTime Series Analysis+1