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检索条件"任意字段=International Conference on Parallel Distributed and Grid Computing"

共 34221 条记录，以下是4881-4890 订阅

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IndigoStore: Latency Optimized distributed Storage Backend for Cloud-Scale Block Storage

IndigoStore: Latency Optimized Distributed Storage Backend f...

引用

international conference on parallel and distributed Systems (ICPADS)

作者： Yichao Li Li Wang Fanfan Cheng Ji Wu Yiyun Zhang Yiming Zhang Didi Chuxing School of Computer NUDT NiceX Lab NUDT

ISBN: (纸本)9781665408790

The major usage of a distributed block storage integrated with a cloud computing platform is to provide the storage for VM (virtual machine) instances. Traditional desktop and server applications tend to be written with small I/O being dominant, and in limited parallelism. Hence the performance of block storage serving these applications migrated to cloud is largely determined by latency of small I/O. This paper presents IndigoStore, an optimized Ceph backend to implement cloud-scale block storage that provides virtual disks for cloud VMs. The design of IndigoStore aims to optimize Ceph BlueStore backend, the state-of-the-art distributed storage backend, to reduce both average and tail latency of small I/O, meanwhile not waste disk bandwidth serving large I/O. We use both microbenchmarks and our production workloads to demonstrate that IndigoStore achieves 29%∼44 % lower average latency, and up to 1.23×lower 99.99 th percentile tail latency than BlueStore, without any notable negative effects on other performance metrics.

关键词： Measurement Cloud computing conferences Tail Production parallel processing Throughput

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distributed Sparse Block grids on GPUs 36th

Distributed Sparse Block Grids on GPUs

引用

36th international conference on High Performance computing, ISC High Performance 2021

作者： Incardona, Pietro Bianucci, Tommaso Sbalzarini, Ivo F. Technische Universität Dresden Faculty of Computer Science Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics Dresden Germany Center for Systems Biology Dresden Dresden Germany Cluster of Excellence Physics of Life TU Dresden Dresden Germany Center for Scalable Data Analytics and Artificial Intelligence Dresden Germany

ISBN: (纸本)9783030787127

We present a design and implementation of distributed sparse block grids that transparently scale from a single CPU to multi-GPU clusters. We support dynamic sparse grids as, e.g., occur in computer graphics with complex deforming geometries and in multi-resolution numerical simulations. We present the data structures and algorithms of our approach, focusing on the optimizations required to render them computationally efficient on CPUs and GPUs alike. We provide a scalable implementation in the OpenFPM software library for HPC. We benchmark our implementation on up to 16 Nvidia GTX 1080 GPUs and up to 64 Nvidia A100 GPUs showing state-of-the-art scalability (68% to 96% parallel efficiency) on three benchmark problems. On a single GPU, our implementation is 14 to 140-fold faster than on a multi-core CPU. © 2021, Springer Nature Switzerland AG.

关键词： Graphics processing unit

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In-Memory parallel computing for Partial Wave Analysis 19

In-Memory Parallel Computing for Partial Wave Analysis

引用

19th international Workshop on Advanced computing and Analysis Techniques in Physics Research (ACAT)

作者： Wei, Zhanchen Huang, Qiulan Sun, Gongxing Liu, Xiaoyu Inst High Energy Phys 19B Yuquan Rd Beijing 100049 Peoples R China Univ Chinese Acad Sci 19A Yuquan Rd Beijing 100049 Peoples R China

The traditional partial wave analysis (PWA) algorithm is designed to process data serially which requires a large amount of memory that may exceed the memory capacity of one single node to store runtime data. It is quite necessary to parallelize this algorithm in a distributed data computing framework to improve its performance. Within an existing production-level Hadoop cluster, we implement PWA algorithm on top of Spark to process data storing on low-level storage system HDFS. But in this case, sharing data through HDFS or internal data communication mechanism of Spark is extremely inefficient. In order to solve this problem, this paper presents an in-memory parallel computing method for PWA algorithm. With this system, we can easily share runtime data in parallel algorithms. We can ensure complete data locality to keep compatibility with the traditional data input/output way and cache most repeatedly used data in memory to improve the performance, owe to the data management mechanism of Alluxio.

关键词： Clustering algorithms

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A Deep Learning Framework Supporting Model Ownership Protection and Traitor Tracing 26

A Deep Learning Framework Supporting Model Ownership Protect...

引用

26th IEEE international conference on parallel and distributed Systems (IEEE ICPADS)

作者： Xu, Guowen Li, Hongwei Zhang, Yuan Lin, Xiaodong Deng, Robert H. Shen, Xuemin Univ Elect Sci & Technol China Chengdu 611731 Peoples R China Univ Guelph 50 Stone Rd E Guelph ON N1G 2W1 Canada Singapore Management Univ Singapore 178902 Singapore Univ Waterloo Waterloo ON N2L 3G1 Canada

ISBN: (纸本)9781728190747

Cloud-based deep learning (DL) solutions have been widely used in applications ranging from image recognition to speech recognition. Meanwhile, as commercial software and services, such solutions have raised the need for intellectual property rights protection of the underlying DL models. Water-marking is the mainstream of existing solutions to address this concern, by primarily embedding pre-defined secrets in a model's training process. However, existing efforts almost exclusively focus on detecting whether a target model is pirated, without considering traitor tracing. In this paper, we present SecureMark_DL, which enables a model owner to embed a unique fingerprint for every customer within parameters of a DL model, extract and verify the fingerprint from a pirated model, and hence trace the rogue customer who illegally distributed his model for profits. We demonstrate that SecureMark_DL is robust against various attacks including fingerprints collusion and network transformation (e.g., model compression and model fine-tuning) Extensive experiments conducted on MNIST and CIFAR10 datasets, as well as various types of deep neural network show the superiority of SecureMark_DL in terms of training accuracy and robustness against various types of attacks.

关键词： Ownership Protection Traitor Tracing Water-marking Deep Learning Cloud computing

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Querying Shared Data with Security Heterogeneity 20

Querying Shared Data with Security Heterogeneity

引用

ACM SIGMOD international conference on Management of Data (SIGMOD)

作者： Cao, Yang Fan, Wenfei Wang, Yanghao Yi, Ke Univ Edinburgh Edinburgh Midlothian Scotland Shenzhen Univ SICS Shenzhen Peoples R China Beihang Univ BDBC Beijing Peoples R China HKUST Hong Kong Peoples R China

ISBN: (纸本)9781450367356

There has been increasing need for secure data sharing. In practice a group of data owners often adopt a heterogeneous security scheme under which each pair of parties decide their own protocol to share data with diverse levels of trust. The scheme also keeps track of how the data is used. This paper studies distributed SQL query answering in the heterogeneous security setting. We define query plans by incorporating toll functions determined by data sharing agreements and reflected in the use of various security facilities. We formalize query answering as a bi-criteria optimization problem, to minimize both data sharing toll and parallel query evaluation cost. We show that this problem is PSPACE-hard for SQL and Sigma(p)(3)-hard for SPC, and it is in NEXPTIME. Despite the hardness, we develop a set of approximate algorithms to generate distributed query plans that minimize data sharing toll and reduce parallel evaluation cost. Using real-life and synthetic data, we empirically verify the effectiveness, scalability and efficiency of our algorithms.

关键词： data sharing security protocol query processing distributed databases privacy-preserving

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Description for Smart grid: Towards the Ontological Approach 8

Description for Smart Grid: Towards the Ontological Approach

引用

8th international conference on Information Technology and Multimedia, ICIMU 2020

作者： Yee Chong, Alicia Tang Mahmoud, Moamin A Lim, Fung-Cheng Kasim, Hairoladenan Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia College of Computing and Informatics Universiti Tenaga Nasional Malaysia

ISBN: (纸本)9781728173108

Smart grids facilitate the use of distributed and renewable resources on the supply side and providing consumers with a range of tailored services on the consumption side. The introduction of energy smart grid in Malaysia allows other stakeholders to reap the potential benefits it offers. However, for the stakeholders to share knowledge and collaborate, a common language that can act as intermediary that allows any application to communicate with each other is needed. Ontologies have been successful in integrating the knowledge required for solving complex problems such as energy management problems. Ontology has become a promising future direction towards devising a context-aware middleware platform for the smart grid. Hence, we propose to develop ontology for energy smart grid to support information integration for existing smart grid applications and to produce an efficient, semantically aware and operationally accurate software environment for managing flexibility in Malaysian energy distribution networks. © 2020 IEEE.

关键词： Ontology

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UniGPS: A Unified Programming Framework for distributed Graph Processing

UniGPS: A Unified Programming Framework for Distributed Grap...

引用

international conference on parallel and distributed Systems (ICPADS)

作者： Zhaokang Wang Junhong Li Yifan Qi Guanghui Zhu Chunfeng Yuan Yihua Huang State Key Laboratory for Novel Software Technology Nanjing University Nanjing P. R. China Nanjing University Nanjing P. R. China

ISBN: (纸本)9781665408790

The industry and academia have proposed many distributed graph processing systems. However, the existing systems are not friendly enough for users like data analysts and algorithm engineers. On the one hand, the programming models and interfaces differ a lot in the existing systems, leading to high learning costs and program migration costs. On the other hand, these graph processing systems are tightly bound to the underlying distributed computing platforms, requiring users to be familiar with distributed computing. To improve the usability of distributed graph processing, we propose a unified distributed graph programming framework UniGPS. Firstly, we propose a unified cross-platform graph programming model VCProg for UniGPS. VCProg hides details of distributed computing from users. It is compatible with the popular graph programming models Pregel, GAS, and Push-Pull. VCProg-based programs can be executed by compatible distributed graph processing systems without modification, reducing the learning overheads of users. Secondly, UniGPS supports Python as the programming language. We propose an interprocess-communication-based execution environment isolation mechanism to enable Java/C++-based graph processing systems to call user-defined methods written in Python. The experimental results show that UniGPS enables users to process big graphs beyond the memory capacity of a single machine without sacrificing usability. UniGPS shows near-linear data scalability and machine scalability.

关键词： Industries Costs Computational modeling Scalability conferences Programming distributed computing

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Research on parallel Processing Method for Ultra-large-scale Drug Screening Data 4

Research on Parallel Processing Method for Ultra-large-scale...

引用

4th international conference on Computer Information Science and Artificial Intelligence, CISAI 2021

作者： Dai, Xuebing Liu, Hao Shan, Liyang Yang, Yanbo Guo, Jingjing Ocean University of China College of Computer Science and Technology Qingdao China

ISBN: (纸本)9781665406925

Artificial intelligence has become the core driving force of a new round of industrial innovation around the world and a strategic technology that leads future development. The application of artificial intelligence to new drug discovery has become the world's medical field. In order to reduce costs, it is the main technical means to quickly discover new drugs. Virtual screening is an important step in new drug discovery. However, it needs to solve many problems, such as the large scale of the compound, the uneven quality, and the complicated processing steps. This paper studies a parallel processing method for ultra-large-scale drug screening data to improve the accuracy of drug virtual screening and the efficiency of drug discovery. Research distributed storage architecture and data balanced distribution, small molecule batch standardized pre-processing and structure optimization. And through experiments to compare the execution efficiency of different modes of the method, the results show that the method can process compound data efficiently and quickly, and improve the efficiency of virtual screening. Summarize the advantages and disadvantages of the method, and point out the general applicability of the method in the next step, and expand the diversification of compound processing functions. © 2021 IEEE.

关键词： Efficiency

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A Study on Blockchain Application in Donation Platform

A Study on Blockchain Application in Donation Platform

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Software Engineering, Artificial Intelligence, Networking and parallel/distributed computing (SNPD-Winter), ACIS international Winter conference on

作者： Wooyoung Lee Dukjin Kim Byeong Ryun Jeon Graduate School Soongsil Univ. Seoul South Korea

ISBN: (纸本)9781665418430;9781665448260

Recently, interest in the spread of the culture of sharing has been increasing at a social level due to the deepening income polarization, poverty, and the rapid growth of civil society and the importance of public interest activities of private organizations. In 2016, the National Statistical Office conducted a survey on changes in donation attitudes and recognition of donations, items that took up a large part in the area for the spread of the overall donation culture were the convenience of donation methods and the enhancement of transparency of donation organizations. The existing donation system is a way for donors to donate directly to several donor organizations. However, this method has its disadvantage: it is difficult for donors to see how their donations are spent. To address the convenience of such donation methods, based on the platform that is the core of the fourth industrial era, it enables unrestricted interaction in the network environment such as mobile or PC, inducing open participation in talent donation to the general public. Blockchain is a data forgery/modulation prevention technology based on distributed computing technology. Because the block chain records continuously changing data on all participating nodes, it is impossible to arbitrarily manipulate the data by the operators of distributed nodes. Blockchain technology is in the spotlight because it can secure transparency in transactions. In this paper, to solve these problems, we try to introduce the block chain technology into the donation system.

关键词： distributed databases Blockchain Organizations Artificial intelligence Software engineering

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Agent-Navigable Dynamic Graph Construction and Visualization over distributed Memory 8

Agent-Navigable Dynamic Graph Construction and Visualization...

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

作者： Gilroy, Justin Paronyan, Satine Acoltzi, Jonathan Fukuda, Munehiro Univ Washington Bothell Comp & Software Syst Bothell WA 98011 USA

ISBN: (纸本)9781728162515

Some graph analyses, such as social network and biological network, need large-scale graph construction and maintenance over distributed memory space. distributed data-streaming tools, including MapReduce and Spark, restrict some computational freedom of incremental graph modification and run-time graph visualization. Instead, we take an agent-based approach. We construct a graph from a scientific dataset in CSV, tab, and XML formats;dispatch many reactive agents on it;and analyze the graph in the form of their collective group behavior: propagation, flocking, and collision. The key to success is how to automate the run-time construction and visualization of agent-navigable graphs mapped over distributed memory. We implemented this distributed graph-computing support in the multi-agent spatial simulation (MASS) library, coupled with the Cytoscape graph visualization software. This paper presents the MASS implementation techniques and demonstrates its execution performance in comparison to MapReduce and Spark, using two benchmark programs: (1) an incremental construction of a complete graph and (2) a KD tree construction.

关键词： multi-agent systems agent-based modeling data analysis data visualization parallel programming

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