Papers Distributed Computing
“Distributed Computing” 태그가 달린 논문 379편 · 필터 해제
Folding@home: achievements from over twenty years of citizen science herald the exascale era
Simulations of biomolecules have enormous potential to inform our understanding of biology but require extremely demanding calculations. For over twenty years, the Folding@home distributed computing project has pioneered…
Distributed ComputingGPUProtein FoldingHulk: Graph Neural Networks for Optimizing Regionally Distributed Computing Systems
Large deep learning models have shown great potential for delivering exceptional results in various applications. However, the training process can be incredibly challenging due to the models' vast parameter sizes, often…
Distributed ComputingGraph Neural NetworkDISCO: Distributed Inference with Sparse Communications
Deep neural networks (DNNs) have great potential to solve many real-world problems, but they usually require an extensive amount of computation and memory. It is of great difficulty to deploy a large DNN model to a singl…
Distributed Computingimage-classificationImage ClassificationImage Super-Resolution+5Welfare and Fairness Dynamics in Federated Learning: A Client Selection Perspective
Federated learning (FL) is a privacy-preserving learning technique that enables distributed computing devices to train shared learning models across data silos collaboratively. Existing FL works mostly focus on designing…
Distributed ComputingFairnessFederated LearningPrivacy PreservingSimilarity, Compression and Local Steps: Three Pillars of Efficient Communications for Distributed Variational Inequalities
Variational inequalities are a broad and flexible class of problems that includes minimization, saddle point, and fixed point problems as special cases. Therefore, variational inequalities are used in various application…
Distributed ComputingDistributed sequential federated learning
The analysis of data stored in multiple sites has become more popular, raising new concerns about the security of data storage and communication. Federated learning, which does not require centralizing data, is a common …
Distributed ComputingFederated LearningDistributed Swarm Intelligence
This paper presents the development of a distributed application that facilitates the understanding and application of swarm intelligence in solving optimization problems. The platform comprises a search space of customi…
Distributed ComputingA smart resource management mechanism with trust access control for cloud computing environment
The core of the computer business now offers subscription-based on-demand services with the help of cloud computing. We may now share resources among multiple users by using virtualization, which creates a virtual instan…
Cloud ComputingDistributed ComputingManagementDesign and Prototyping Distributed CNN Inference Acceleration in Edge Computing
For time-critical IoT applications using deep learning, inference acceleration through distributed computing is a promising approach to meet a stringent deadline. In this paper, we implement a working prototype of a new …
Distributed ComputingEdge-computingModel CompressionModel Selection+1Sequential Gradient Coding For Straggler Mitigation
In distributed computing, slower nodes (stragglers) usually become a bottleneck. Gradient Coding (GC), introduced by Tandon et al., is an efficient technique that uses principles of error-correcting codes to distribute g…
Distributed Computingfseval: A Benchmarking Framework for Feature Selection and Feature Ranking Algorithms
The fseval Python package allows benchmarking Feature Selection and Feature Ranking algorithms on a large scale, and facilitates the comparison of multiple algorithms in a systematic way. In particular, fseval enables us…
Automated Feature EngineeringBenchmarkingClassification with Costly FeaturesDistributed Computing+3Block size estimation for data partitioning in HPC applications using machine learning techniques
The extensive use of HPC infrastructures and frameworks for running dataintensive applications has led to a growing interest in data partitioning techniques and strategies. In fact, application performance can be heavily…
Distributed ComputingA Stable, Fast, and Fully Automatic Learning Algorithm for Predictive Coding Networks
Predictive coding networks are neuroscience-inspired models with roots in both Bayesian statistics and neuroscience. Training such models, however, is quite inefficient and unstable. In this work, we show how by simply c…
Distributed Computingimage-classificationImage ClassificationSchedulingDistributed Computing for Scalable Optimal Power Flow in Large Radial Electric Power Distribution Systems with Distributed Energy Resources
Solving the non-convex optimal power flow (OPF) problem for large-scale power distribution systems is computationally expensive. An alternative is to solve the relaxed convex problem or linear approximated problem, but t…
Distributed ComputingDistributed Graph Neural Network Training: A Survey
Graph neural networks (GNNs) are a type of deep learning models that are trained on graphs and have been successfully applied in various domains. Despite the effectiveness of GNNs, it is still challenging for GNNs to eff…
CPUDistributed ComputingGPUGraph Neural Network+1A partial order view of message-passing communication models
There is a wide variety of message-passing communication models, ranging from synchronous ''rendez-vous'' communications to fully asynchronous/out-of-order communications. For large-scale distributed systems, the communi…
Distributed ComputingDistributed Estimation and Inference for Semi-parametric Binary Response Models
The development of modern technology has enabled data collection of unprecedented size, which poses new challenges to many statistical estimation and inference problems. This paper studies the maximum score estimator of …
Distributed ComputingLamarckian Platform: Pushing the Boundaries of Evolutionary Reinforcement Learning towards Asynchronous Commercial Games
Despite the emerging progress of integrating evolutionary computation into reinforcement learning, the absence of a high-performance platform endowing composability and massive parallelism causes non-trivial difficulties…
CPUDistributed Computingreinforcement-learningReinforcement Learning+2Performance Evaluation of Query Plan Recommendation with Apache Hadoop and Apache Spark
Access plan recommendation is a query optimization approach that executes new queries using prior created query execution plans (QEPs). The query optimizer divides the query space into clusters in the mentioned method. H…
ClusteringDistributed ComputingUnconventional application of k-means for distributed approximate similarity search
Similarity search based on a distance function in metric spaces is a fundamental problem for many applications. Queries for similar objects lead to the well-known machine learning task of nearest-neighbours identificatio…
Distributed Computing