Papers Distributed Computing
“Distributed Computing” 태그가 달린 논문 379편 · 필터 해제
Toward Creating Subsurface Camera
In this article, the framework and architecture of Subsurface Camera (SAMERA) is envisioned and described for the first time. A SAMERA is a geophysical sensor network that senses and processes geophysical sensor signals,…
Distributed ComputingSeismic ImagingCommunication Efficient Parallel Algorithms for Optimization on Manifolds
The last decade has witnessed an explosion in the development of models, theory and computational algorithms for "big data" analysis. In particular, distributed computing has served as a natural and dominating paradigm f…
Distributed ComputingLow-Rank Matrix CompletionMatrix CompletionvalidMMLSpark: Unifying Machine Learning Ecosystems at Massive Scales
We introduce Microsoft Machine Learning for Apache Spark (MMLSpark), an ecosystem of enhancements that expand the Apache Spark distributed computing library to tackle problems in Deep Learning, Micro-Service Orchestratio…
BIG-bench Machine LearningDistributed Computingobject-detectionObject DetectionQuantile Regression Under Memory Constraint
This paper studies the inference problem in quantile regression (QR) for a large sample size $n$ but under a limited memory constraint, where the memory can only store a small batch of data of size $m$. A natural method …
Distributed Computingquantile regressionregressionA Periodicity-based Parallel Time Series Prediction Algorithm in Cloud Computing Environments
In the era of big data, practical applications in various domains continually generate large-scale time-series data. Among them, some data show significant or potential periodicity characteristics, such as meteorological…
Cloud ComputingData CompressionDistributed ComputingPrediction+3A Bi-layered Parallel Training Architecture for Large-scale Convolutional Neural Networks
Benefitting from large-scale training datasets and the complex training network, Convolutional Neural Networks (CNNs) are widely applied in various fields with high accuracy. However, the training process of CNNs is very…
Distributed ComputingSchedulingA System for Massively Parallel Hyperparameter Tuning
Modern learning models are characterized by large hyperparameter spaces and long training times. These properties, coupled with the rise of parallel computing and the growing demand to productionize machine learning work…
BIG-bench Machine LearningDistributed ComputingHyperparameter OptimizationA Droplet Approach Based on Raptor Codes for Distributed Computing With Straggling Servers
We propose a coded distributed computing scheme based on Raptor codes to address the straggler problem. In particular, we consider a scheme where each server computes intermediate values, referred to as droplets, that ar…
Distributed ComputingPredicate learning in neural systems: Discovering latent generative structures
Humans learn complex latent structures from their environments (e.g., natural language, mathematics, music, social hierarchies). In cognitive science and cognitive neuroscience, models that infer higher-order structures …
Distributed ComputingTowards automated neural design: An open source, distributed neural architecture research framework
NORD (Neural Operations Research & Development) is an open source distributed deep learning architectural research framework, based on PyTorch, MPI and Horovod. It aims to make research of deep architectures easier for e…
Distributed ComputingLeft Ventricle Segmentation and Volume Estimation on Cardiac MRI using Deep Learning
In the United States, heart disease is the leading cause of death for both men and women, accounting for 610,000 deaths each year [1]. Physicians use Magnetic Resonance Imaging (MRI) scans to take images of the heart in …
Distributed ComputingGPULeft Ventricle SegmentationLV Segmentation+1PARyOpt: A software for Parallel Asynchronous Remote Bayesian Optimization
PARyOpt is a python based implementation of the Bayesian optimization routine designed for remote and asynchronous function evaluations. Bayesian optimization is especially attractive for computational optimization due t…
Bayesian OptimizationDistributed ComputingSurface ReconstructionA MapReduce based Big-data Framework for Object Extraction from Mosaic Satellite Images
We propose a framework stitching of vector representations of large scale raster mosaic images in distributed computing model. In this way, the negative effect of the lack of resources of the central system and scalabili…
Distributed ComputingOn the Effect of Task-to-Worker Assignment in Distributed Computing Systems with Stragglers
We study the expected completion time of some recently proposed algorithms for distributed computing which redundantly assign computing tasks to multiple machines in order to tolerate a certain number of machine failures…
Distributed ComputingOrchestral: a lightweight framework for parallel simulations of cell-cell communication
We develop a modeling and simulation framework capable of massively parallel simulation of multicellular systems with spatially resolved stochastic kinetics in individual cells. By the use of operator-splitting we decoup…
Distributed ComputingHolographic Automata for Ambient Immersive A. I. via Reservoir Computing
We prove the existence of a semilinear representation of Cellular Automata (CA) with the introduction of multiple convolution kernels. Examples of the technique are presented for rules akin to the "edge-of-chaos" includi…
Distributed ComputingGeneralized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression
In order to scale standard Gaussian process (GP) regression to large-scale datasets, aggregation models employ factorized training process and then combine predictions from distributed experts. The state-of-the-art aggre…
Distributed ComputingregressionPredictive Performance Modeling for Distributed Computing using Black-Box Monitoring and Machine Learning
In many domains, the previous decade was characterized by increasing data volumes and growing complexity of computational workloads, creating new demands for highly data-parallel computing in distributed systems. Effecti…
BIG-bench Machine LearningDistributed ComputingSchedulingRobust Gradient Descent via Moment Encoding with LDPC Codes
This paper considers the problem of implementing large-scale gradient descent algorithms in a distributed computing setting in the presence of {\em straggling} processors. To mitigate the effect of the stragglers, it has…
Distributed ComputingBillion-scale Network Embedding with Iterative Random Projection
Network embedding, which learns low-dimensional vector representation for nodes in the network, has attracted considerable research attention recently. However, the existing methods are incapable of handling billion-scal…
Distributed ComputingLink PredictionNetwork EmbeddingNode Classification