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检索条件"任意字段=Distributed Memory Computing Conference"

共 8890 条记录，以下是4921-4930 订阅

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Two Birds with One Stone: Boosting Both Search and Write Performance for Tree Indices on Persistent memory

Two Birds with One Stone: Boosting Both Search and Write Per...

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作者： Luo, Yongping Jin, Peiquan Zhang, Zhou Zhang, Junchen Cheng, Bin Zhang, Qinglin Department of Computer Science and Technology University of Science and Technology of China Hefei230027 China Tencent Inc. Shenzhen518057 China

The advance of byte-addressable persistent memory (PM) makes it a hot topic to revisit traditional tree indices such as B+-tree and radix tree, and a few new persistent memory-friendly tree indices have been proposed. However, due to the special features of persistent memory compared to DRAM and the limitations of B+-tree-like indices, it is much harder to optimize both search and write performance for tree indices on persistent memory. As a result, most existing indices for persistent memory, e.g., WB-tree, proposed to improve write performance while sacrificing search performance. Aiming to optimize both write and search performance for tree indices on persistent memory, in this paper, we first propose a novel Two-Layer Architecture (TLA) for constructing tree indices on persistent memory. The key idea, of TLA is to organize the index with a search-optimized top layer and a write-optimized bottom layer, letting the top layer optimize search performance and the bottom layer improve write performance. By adopting efficient structures for the two layers, TLA can boost both write and search performance for tree indices on persistent memory. Following the TLA architecture, we present a new index called TLBtree (Two-Layer B+-tree) offering high search and write performance for persistent memory. Moreover, we develop a concurrent TLBtree to support non-blocking read operations in multi-core environment. We evaluate our proposals under a server equipped with real Intel Optane persistent memory. The results show that TLBtree outperforms the state-of-the-art tree indices, including WB-tree, Fast&Fair, and FPTree, in both search and write performance. Also, the concurrent TLBtree can achieve up to 3.7x speedup than its competitors under the multi-core environment. © 2021 Association for computing Machinery.

关键词： Dynamic random access storage

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FedDIP: Federated Learning with Extreme Dynamic Pruning and Incremental Regularization 23

FedDIP: Federated Learning with Extreme Dynamic Pruning and ...

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23rd IEEE International conference on Data Mining, ICDM 2023

作者： Long, Qianyu Anagnostopoulos, Christos Parambath, Shameem Puthiya Bi, Daning University of Glasgow School of Computing Science United Kingdom Hunan University College of Finance and Statistics China

ISBN: (纸本)9798350307887

Federated Learning (FL) has been successfully adopted for distributed training and inference of large-scale Deep Neural Networks (DNNs). However, DNNs are characterized by an extremely large number of parameters, thus, yielding significant challenges in exchanging these parameters among distributed nodes and managing the memory. Although recent DNN compression methods (e.g., sparsification, pruning) tackle such challenges, they do not holistically consider an adaptively controlled reduction of parameter exchange while maintaining high accuracy levels. We, therefore, contribute with a novel FL framework (coined FedDIP), which combines (i) dynamic model pruning with error feedback to eliminate redundant information exchange, which contributes to significant performance improvement, with (ii) incremental regularization that can achieve extreme sparsity of models. We provide convergence analysis of FedDIP and report on a comprehensive performance and comparative assessment against state-of-the-art methods using benchmark data sets and DNN models. Our results showcase that FedDIP not only controls the model sparsity but efficiently achieves similar or better performance compared to other model pruning methods adopting incremental regularization during distributed model training. The code is available at: https://***/EricLoong/feddip. © 2023 IEEE.

关键词： Deep neural networks

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Performance limitations of block-multithreaded distributed-memory systems 09

Performance limitations of block-multithreaded distributed-m...

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Simulation Winter conference

作者： W. M. Zuberek Department of Computer Science Memorial University Saint John's Canada Department of Applied Informatics University of Life Sciences Warsaw Poland

ISBN: (纸本)9781424457717

The performance of modern computer systems is increasingly often limited by long latencies of accesses to the memory subsystems. Instruction-level multithreading is an architectural approach to tolerating such long latencies by switching instruction threads rather than waiting for the completion of memory operations. The paper studies performance limitations in distributed-memory block multithreaded systems and determines conditions for such systems to be balanced. Event-driven simulation of a timed Petri net model of a simple distributed-memory system confirms the derived performance results.

关键词： Delay Multithreading Computational modeling Yarn Discrete event simulation Cache memory Computer science Computer simulation distributed computing Manufacturing processes

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16th International conference on Computational Science and Its Applications, ICCSA 2016

16th International Conference on Computational Science and I...

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16th International conference on Computational Science and Its Applications, ICCSA 2016

ISBN: (纸本)9783319421070

The proceedings contain 51 papers. The special focus in this conference is on High Performance computing, Networks, Geometric Modeling, Graphics and Visualization. The topics include: Parallel sparse matrix-vector multiplication using accelerators;on the cluster-connectivity of wireless sensor networks;a PBIL for load balancing in network coding based multicasting;a proposed protocol for periodic monitoring of cloud storage services using trust and encryption;implementation of multiple-precision floating-point arithmetic on Intel Xeon phi coprocessors;towards a sustainable architectural design by an adaptation of the architectural driven design method;memory-aware scheduling for mixed-criticality systems;a generalized ant routing mechanism framework in mobile p2p networks;computational verification of network programs for several openflow switches in coq;parallelizing simulated annealing algorithm in many integrated core architecture;distributed computing infrastructure based on dynamic container clusters;building a virtual cluster for 3d graphics applications;great deluge and extended great deluge based job scheduling in grid computing using gridsim;employing docker swarm on openstack for biomedical analysis;user attribution in collaborative report writing for emergency management;an improved reconfiguration algorithm for VLSI arrays with a-star;performance evaluation of MAC protocols in energy harvesting wireless sensor networks;an enabler for next generation internet of things;Petri nets for modelling of message passing middleware in cloud computing environments;a comparative study of LOWESS and RBF approximations for visualization and automatic temporal segmentation of articulated hand motion.

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Learning-Based and Data-Driven TCP Design for memory-Constrained IoT

Learning-Based and Data-Driven TCP Design for Memory-Constra...

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International conference on distributed computing in Sensor Systems (DCOSS)

作者： Wei Li Fan Zhou Waleed Meleis Kaushik Chowdhury Dept. of Electrical and Computer Engineering Northeastern University Boston USA

ISBN: (纸本)9781509014613

Advances in wireless technology have resulted in pervasive deployment of devices of a high variability in form factors, memory and computational ability. The need for maintaining continuous connections that deliver data with high reliability necessitate re-thinking of conventional design of the transport layer protocol. This paper investigates the use of Q-learning in TCP cwnd adaptation during the congestion avoidance state, wherein the classical alternation of the window is replaced, thereby allowing the protocol to immediately respond to previously seen network conditions. Furthermore, it demonstrates how memory plays a critical role in building the exploration space, and proposes ways to reduce this overhead through function approximation. The superior performance of the learning-based approach over TCP New Reno is demonstrated through a comprehensive simulation study, revealing 33.8% and 12.1% improvement in throughput and delay, respectively, for the evaluated topologies. We also show how function approximation can be used to dramatically reduce the memory requirements of a learning-based protocol while maintaining the same throughput and delay.

关键词： Protocols Throughput Internet of things Delays Function approximation memory management Wireless sensor networks

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Performance variability of highly parallel architectures

Performance variability of highly parallel architectures

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International conference on Computational Science, ICCS 2003

作者： Kramer, William T. C. Ryan, Clint Department of Computing Sciences University of California at Berkeley United States The National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory United States

ISBN: (纸本)9783540401964

The design and evaluation of high performance computers has concentrated on increasing computational speed for applications. This performance is often measured on a well configured dedicated system to show the best case. In the real environment, resources are not always dedicated to a single task, and systems run tasks that may influence each other, so run times vary, sometimes to an unreasonably large extent. This paper explores the amount of variation seen across four large distributed memory systems in a systematic manner. It then analyzes the causes for the variations seen and discusses what can be done to decrease the variation without impacting performance. © Springer-Verlag Berlin Heidelberg 2003.

关键词： Parallel architectures

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21st International conference on Big Data Analytics and Knowledge Discovery, DaWaK 2019

21st International Conference on Big Data Analytics and Know...

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21st International conference on Big Data Analytics and Knowledge Discovery, DaWaK 2019

ISBN: (纸本)9783030275198

The proceedings contain 22 papers. The special focus in this conference is on Big Data Analytics and Knowledge Discovery. The topics include: Mining Quantitative Temporal Dependencies Between Interval-Based Streams;Democratization of OLAP DSMS;leveraging the Data Lake: Current State and Challenges;SDWP: A New Data Placement Strategy for distributed Big Data Warehouses in Hadoop;improved Programming-Language Independent MapReduce on Shared-memory Systems;evaluating Redundancy and Partitioning of Geospatial Data in Document-Oriented Data Warehouses;scalable Least Square Twin Support Vector Machine Learning;finding Strongly Correlated Trends in Dynamic Attributed Graphs;text-Based Event Detection: Deciphering Date Information Using Graph Embeddings;a Hybrid Architecture for Tactical and Strategic Precision Agriculture;efficiently computing Homomorphic Matches of Hybrid Pattern Queries on Large Graphs;From Conceptual to Logical ETL Design Using BPMN and Relational Algebra;accurate Aggregation Query-Result Estimation and Its Efficient Processing on distributed Key-Value Store;urban Analytics of Big Transportation Data for Supporting Smart Cities;frequent Item Mining When Obtaining Support Is Costly;mining Sequential Patterns of Historical Purchases for E-commerce Recommendation;discovering and Visualizing Efficient Patterns in Cost/Utility Sequences;Efficient Row Pattern Matching Using Pattern Hierarchies for Sequence OLAP;statistically Significant Discriminative Patterns Searching;Multidimensional Integration of RDF Datasets.

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Split Objects for Multiconsistent Shared memory

Split Objects for Multiconsistent Shared Memory

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International conference on Computer Engineering and Applications (ICCEA)

作者： Nico Kaemmer Patrick Schmidt Steffen Gerhold Thilo Schmitt Peter Schulthess Institute of Distributed Systems Ulm University Ulm Germany

ISBN: (纸本)9781424460793;9780769539829

distributed systems with shared memory and more than one consistency model for shared data are often restricted in use or inflexible for programmers. This paper describes details of our transactional distributed memory system, that provides several consistency models for shared memory. To this end Rainbow OS implements so-called split objects guaranteeing the integrity of heap structures and providing several consistency models for data.

关键词： Kernel memory management Programming profession Operating systems Runtime Data structures Computer applications distributed computing Data engineering Application software

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Architecture of a large-scale location service

Architecture of a large-scale location service

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International conference on distributed computing Systems

作者： A. Leonhardi K. Rothermel Institute of Parallel and Distributed High-Performance Systems University of Stuttgart Stuttgart Germany

Location-aware services are a promising way of exploiting the special possibilities created by ubiquitous mobile devices and wireless communication. Advanced location-aware applications will require highly accurate information about the geographic location of mobile objects and functionality that goes beyond simply querying the user's position, for example determining all mobile objects inside a certain geographic area. In this paper, we propose a generic large-scale location service, which has been designed with the goal of managing the highly dynamic location information for a large number of mobile objects, thus providing a common infrastructure that can be employed by location-aware applications. We propose a hierarchical distributed architecture, which can efficiently process these queries in a scalable way. To be able to deal with the frequent updates and queries resulting from highly dynamic location information, we propose a data storage component, which makes use of a main memory database.

关键词： Large-scale systems Computer architecture memory distributed computing Information management Nearest neighbor searches Database systems Spatial databases Prototypes Network servers

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4th International conference on Soft computing and its Engineering Applications, icSoftComp 2022

4th International Conference on Soft Computing and its Engin...

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4th International conference on Soft computing and its Engineering Applications, icSoftComp 2022

ISBN: (纸本)9783031276088

The proceedings contain 36 papers. The special focus in this conference is on Soft computing and its Engineering Applications. The topics include: Explainable AI for Predictive Analytics on Employee Attrition;graph Convolutional Neural Networks for Nuclei Segmentation from Histopathology Images;Performance Analysis of Cache memory in CPU;extraction of Single Chemical Structure from Handwritten Complex Chemical Structure with Junction Point Based Segmentation;automatic Mapping of Deciduous and Evergreen Forest by Using Machine Learning and Satellite Imagery;RIN: Towards a Semantic Rigorous Interpretable Artificial Immune System for Intrusion Detection;SVRCI: An Approach for Semantically Driven Video Recommendation Incorporating Collective Intelligence;deep Learning Based Model for Fundus Retinal Image Classification;one True Pairing: Evaluating Effective Language Pairings for Fake News Detection Employing Zero-Shot Cross-Lingual Transfer;reliable Network-Packet Binary Classification;semKnowNews: A Semantically Inclined Knowledge Driven Approach for Multi-source Aggregation and Recommendation of News with a Focus on Personalization;extraction and Analysis of Speech Emotion Features Using Hybrid Punjabi Audio Dataset;human Activity Recognition in Videos Using Deep Learning;Brain Tumor Classification Using VGG-16 and MobileNetV2 Deep Learning Techniques on Magnetic Resonance Images (MRI);five-Year Life Expectancy Prediction of Prostate Cancer Patients Using Machine Learning Algorithms;an Ensemble MultiLabel Classifier for Intra-Cranial Haemorrhage Detection from Large, Heterogeneous and Imbalanced Database;A Method for Workflow Segmentation and Action Prediction from Video Data - AR Content;convolutional Neural Network Approach for Iris Segmentation;reinforcement Learning Algorithms for Effective Resource Management in Cloud computing;FedCLUS: Federated Clustering from distributed Homogeneous Data;corpus Building for Hate Speech Detection of Gujarati Language;a Software

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