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检索条件"任意字段=Euromicro Conference on Parallel, Distributed and Network-Based Processing"

共 4332 条记录，以下是961-970 订阅

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Poster Abstract: Model Average-based distributed Training for Sparse Deep Neural networks

Poster Abstract: Model Average-based Distributed Training fo...

引用

39th IEEE International conference on Computer Communications (IEEE INFOCOM)

作者： Yang, Yuetong Lai, Zhiquan Cai, Lei Li, Dongsheng Natl Univ Def Technol Sch Comp Natl Key Lab Parallel & Distributed Proc Changsha Peoples R China

ISBN: (数字)9781728186955

ISBN: (纸本)9781728186955

distributed training of large-scale deep neural networks(DNNs) is a challenging work for it's time costing and complicated communication. Existing works have achieved scalable performance on GPU clusters for dense DNNs in the computer vision area. However, little progress has been made on the distributed training of sparse DNNs which is commonly used in the area of natural language processing (NLP). In this poster, we introduce SA-HMA, a sparsity -aware hybrid training method for sparse deep models. SA-HMA combines Model Average (MA) and synchronous optimization methods together, expecting to reduce the communication cost for spare model training. The experimental results show that SA-HMA achieves 133x speedup over the state-of-the-art work.

关键词： Training Natural language processing Convergence Throughput Computational modeling Neural networks Machine learning

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NoC Design Methodologies for Heterogeneous Architecture

NoC Design Methodologies for Heterogeneous Architecture

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euromicro conference on parallel, distributed and network-based processing

作者： Lulwah Alhubail Masoomeh Jasemi Nader Bagherzadeh Computer Engineering Department Kuwait University Kuwait Kuwait Department of Computer Engineering Sharif University of Technology Tehran Iran Electrical Engineering and Computer Science Department University of California Irvine CA USA

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

Fused CPU-GPU architectures that utilize the powerful features of both processors are common nowadays. Using homogeneous interconnect for such heterogeneous processors can result in performance degradation and power increase. This paper explores the optimization of heterogeneous NoC design to connect heterogeneous CPU-GPU architecture in terms of NoC performance and power. This involves solving four different NoC design sub-problems simultaneously; processing elements (PEs) mapping, buffer size and virtual channel assignments, and links' bandwidth determination. Heuristic-based optimization methods were proposed to obtain a near-optimal heterogeneous NoC design, and formal models were used to get a measure of the NoC performance and power of the evaluated design. The obtained optimal designs were validated using a full-system simulator.

关键词： Biological cells Computer architecture Graphics processing units Optimization Sociology Statistics Design methodology

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Massively parallel processing Database for Sequence and Graph Data Structures Applied to Rapid-Response Drug Repurposing 8

Massively Parallel Processing Database for Sequence and Grap...

引用

8th IEEE International conference on Big Data (Big Data)

作者： Rickett, Christopher D. Maschhoff, Kristyn J. Sukumar, Sreenivas R. Hewlett Packard Enterprise Spring TX 77389 USA

ISBN: (纸本)9781728162515

In this paper, we present the application of a massively parallel-processing graph database for rapid-response drug repurposing. The novelty of our approach is that the scalable graph database is able to host a knowledge graph of medically relevant facts integrated from multiple knowledge sources and also act as a computational engine capable of in-database protein sequence analytics. We demonstrate the performance of the graph database on a real-world use-case to hypothesize cures for COVID-19, leveraging its built-in accelerated protein-sequence matching capabilities at unprecedented scale (to simultaneously handle data size and query latency requirements for interactive research). based on supporting evidence from medical literature, we show that results generated by computing similarity of COVID-19 virus proteins across 4 million other open-science sequences and intelligently traversing over a 150 billion facts from open-science medical knowledge produces biologically insightful results. By presenting sample queries and extending application to use-cases beyond COVID-19, we demonstrate the use and value of the novel database for hypotheses generation in reducing the time-to-insight and increasing researcher productivity with interactivity.

关键词： graph database graph analytics in-database analytics distributed processing parallel processing sequence analytics

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A Learning-based Approach for Evaluating the Capacity of Data processing Pipelines 26th

A Learning-Based Approach for Evaluating the Capacity of Dat...

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26th International conference on parallel and distributed Computing (Euro-Par)

作者： Alsayasneh, Maha De Palma, Noel Univ Grenoble Alpes LIG CNRS Grenoble France

ISBN: (纸本)9783030576752;9783030576745

Data processing pipelines are made of various software components with complex interactions and a large number of configuration settings. Identifying when a pipeline has reached its maximum performance capacity is generally a non-trivial task. Metrics exported at the software and at the hardware levels can provide insightful information about the current state of the system, but it can be difficult to interpret the value of a metric, or even to know which metrics to focus on. Considering a popular pipeline composed of Kafka, Spark Streaming, and Cassandra, this paper proposes a learning-based approach to automatically infer the state of such a pipeline solely by analyzing metrics. Our results show that we are able to achieve a high prediction accuracy when predicting on new configurations and when the number of data sources changes. Furthermore, our analysis demonstrates that the best prediction results are obtained when metrics of different types are combined.

关键词： Performance bottleneck Data processing pipeline Machine learning

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Mapreduce hadoop models for distributed neural network processing of big data using cloud services 14th

Mapreduce hadoop models for distributed neural network proce...

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14th International Scientific and Technical conference on Computer Science and Information Technologies, CSIT 2019

作者： Axak, Natalia Korablyov, Mykola Rosinskiy, Dmytro Kharkiv National University of Radio Electronics Nauky Ave. 14 Kharkiv61166 Ukraine

ISBN: (纸本)9783030336943

The paper proposes a formalization process of Big Data distributed intelligent processing using Cloud-Fog-Dew architecture. This process provides specialized services, based on continuous support of experts in areas of concern, and advisory support for their actions in diagnostically complex cases. The method of Big Data processing based on neural networks is considered, which is distinguished by dynamic redistribution of work between computers, that allows to uniformly load the computing cluster with different data topologies. Proposed method is less by one order of computational complexity and less time spent. The MapReduce Hadoop models for distributed neural network processing of Big Data were proposed, characterized by the adaptation of data topology to the corresponding architectural computer cluster. This reduces the amount of information transmitted between nodes to increase productivity in solving complex tasks and effectively balancing the load of computing resources with different data topologies. An experimental Hadoop cluster was created to evaluate the performance of developed models for Big Data distributed processing. It allows for the implementation of parallel learning procedures for multilayer neural networks based on "star" and "fully connected graph" data topology with different amounts of input data. © Springer Nature Switzerland AG 2020.

关键词： MapReduce

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parallel Comparison of Huge DNA Sequences in Multiple GPUs with Block Pruning

Parallel Comparison of Huge DNA Sequences in Multiple GPUs w...

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euromicro conference on parallel, distributed and network-based processing

作者： Marco Figueiredo Edans Sandes George Teodoro Alba C. M. A. Melo Univ. of Brasilia Univ. Fed. de Minas Gerais

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

Sequence comparison is a task performed in several Bioinformatics applications daily all over the world. Algorithms that retrieve the optimal result have quadratic time complexity, requiring a huge amount of computing power when the sequences compared are long. In order to reduce the execution time, many parallel solutions have been proposed in the literature. Nevertheless, depending on the sizes of the sequences, even those parallel solutions take hours or days to complete. Pruning techniques can significantly improve the performance of the parallel solutions and a few approaches have been proposed to provide pruning capabilities for sequence comparison applications. This paper proposes and evaluates a variant of the block pruning approach that runs in multiple GPUs, in homogeneous or heterogeneous environments. Experimental results obtained with DNA sequences in two testbeds show that significant performance gains are obtained with pruning, compared to its non-pruning counterpart, achieving the impressive performance of 694.8 GCUPS (Billions of Cells Updated per Second) for four GPUs.

关键词： Graphics processing units DNA Mathematical model Bioinformatics Time complexity

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Secure deduplication method based on tag clustering

Secure deduplication method based on tag clustering

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IEEE International conference on Big Data and Cloud Computing (BdCloud)

作者： Fengkun Gao Chunbo Wang Xiaoqiang Di Jinhui Cao Xu Liu Hui Qi Jilin Key Laboratory of Network and Information Security Changchun University of Science and Technology Changchun China School of Computer Science and Technology Changchun University of Science and Technology Changchun China School of Information Technology Changchun Automobile Industry Institute Changchun China

Although cloud storage technology can provide users with convenient storage services, a large amount of duplicate data in the cloud brings a huge storage burden and the risk of privacy leakage. To improve the utilization of cloud storage resources and protect data confidentiality, random message lock encryption technology (R-MLE) can be used to delete redundant data in the cloud. But the theoretical basis of the deduplication scheme based on R-MLE is bilinear mapping, so the computational cost of finding duplicate fingerprint-tags is relatively large. To improve the deduplication efficiency, we proposed a secure deduplication scheme based on the autoencoder model in our previous research, using the model to generate the abstract-tags of the data, and using the similarity of the abstract-tags to quickly filter out the fingerprint-tags with high repeatability, which greatly reduces the number of fingerprint-tag comparisons. On this basis, this paper further proposes a secure deduplication method based on k-means clustering. First, the abstract-tags in cloud storage are clustered, and then the distance between the abstract-tags uploaded by users and the centroid is calculated. Then, the abstract-tags of the category with the closest distance are selected. Finally, duplicate data detection is performed only on the fingerprint-tags corresponding to these abstract-tags. In this way, the filtering speed of fingerprint-tags can be further accelerated. Experiments show that our method has higher performance than the secure deduplication method based on the autoencoder model.

关键词： Cloud computing Data privacy Filtering Computational modeling Fingerprint recognition Data models Computational efficiency

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Inter-Server RSS: Extending Receive Side Scaling for Inter-Server Workload Distribution

Inter-Server RSS: Extending Receive Side Scaling for Inter-S...

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euromicro conference on parallel, distributed and network-based processing

作者： Andreas Oeldemann Franz Biersack Thomas Wild Andreas Herkersdorf Chair of Integrated Systems Technical University of Munich Munich Germany

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

network Function Virtualization enables operators to schedule diverse network processing workloads on a general-purpose hardware infrastructure. However, short-lived processing peaks make an efficient dimensioning of processing resources under stringent tail latency constraints challenging. To reduce dimensioning overheads, several load balancing approaches, which either adaptively steer network traffic to a group of servers or to their internal CPU cores, have separately been *** this paper, we present Inter-Server RSS (isRSS), a hardware mechanism built on top of Receive Side Scaling in the network interface card, which combines intra-and inter-server load balancing. In a first step, isRSS targets a balanced utilization of processing resources by steering packet bursts to CPU cores based on per-core load feedback. If all local CPU cores are highly loaded, isRSS avoids high queueing delays by redirecting newly arriving packet bursts to other servers, which execute the same network functions, exploiting that processing peaks are unlikely to occur at all servers at the same time. Our evaluation based on real-world network traces shows that compared to Receive Side Scaling, the joint intra-and inter-server load balancing approach is able to reduce the processing capacity dimensioned for network function execution by up to 38.95% and limit packet reordering to 0.0589% while maintaining tail latencies.

关键词： Servers Load management Hardware Multicore processing network interfaces Resource management Delays

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A Trusted Agent Strategy in Decentralized network Environments

A Trusted Agent Strategy in Decentralized Network Environmen...

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euromicro conference on parallel, distributed and network-based processing

作者： Karishma Shrisha Rao International Institute of Information Technology Bangalore Bangalore India

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165837

A problem currently faced by autonomous systems, learning agents, multi-agent systems and other cognitive systems is the extreme susceptibility to bad information and malicious misdirection. In this paper, we propose a strategy to identify trusted agents in a multi-agent system, so that a system can eliminate the knowledge provided by malicious or unknown agents. The strategy is covered in three phases by each agent; firstly sharing information with other agents in the system, secondly calculating the conclusion based on the majority of information received from others and lastly identifying the trusted, malicious and unknown agents in the system. The theoretical analysis shows that the trusted agent strategy (TAS) is capable of identifying malicious or compromised agents if a system has less than 25% of malicious agents. This is further improved in the advanced-TAS which can handle up to 40% malicious agents. The experimental results show that trusted and malicious agents can be identified with high accuracy.

关键词： Multi-agent systems Arrays Communication channels Decision making Fault tolerance

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Managing Failures in Task-based parallel Workflows in distributed Computing Environments 26th

Managing Failures in Task-Based Parallel Workflows in Distri...

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26th International conference on parallel and distributed Computing (Euro-Par)

作者： Ejarque, Jorge Bertran, Marta Cid-Fuentes, Javier Alvarez Conejero, Javier Badia, Rosa M. Barcelona Supercomp Ctr Barcelona Spain

ISBN: (纸本)9783030576752;9783030576745

Current scientific workflows are large and complex. They normally perform thousands of simulations whose results combined with searching and data analytics algorithms, in order to infer new knowledge, generate a very large amount of data. To this end, workflows comprise many tasks and some of them may fail. Most of the work done about failure management in workflow managers and runtimes focuses on recovering from failures caused by resources (retrying or resubmitting the failed computation in other resources, etc.) However, some of these failures can be caused by the application itself (corrupted data, algorithms which are not converging for certain conditions, etc.), and these fault tolerance mechanisms are not sufficient to perform a successful workflow execution. In these cases, developers have to add some code in their applications to prevent and manage the possible failures. In this paper, we propose a simple interface and a set of transparent runtime mechanisms to simplify how scientists deal with application-based failures in task-based parallel workflows. We have validated our proposal with use-cases from e-science and machine learning to show the benefits of the proposed interface and mechanisms in terms of programming productivity and performance.

关键词： Failure management Scientific workflows parallel programming distributed computing

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