检索结果-内蒙古大学图书馆

26th international conference on parallel and Distributed computing (Euro-Par)

作者： Wang, Qinglin Li, Dongsheng Huang, Xiandong Shen, Siqi Mei, Songzhu Liu, Jie Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China

ISBN: (纸本)9783030576752;9783030576745

Convolutional Neural networks (CNNs) are widely applied in various machine learning applications and very time-consuming. Most of CNNs' execution time is consumed by convolutional layers. A common approach to implementing convolutions is the FFT-based one, which can reduce the arithmetic complexity of convolutions without losing too much precision. As the performance of ARMv8 multi-core CPUs improves, they can also be utilized to perform CNNs like Intel X86 CPUs. In this paper, we present a new parallel FFT-based convolution implementation on ARMv8 multi-core CPUs. the implementation makes efficient use of ARMv8 multi-core CPUs through a series of computation and memory optimizations. the experiment results on two ARMv8 multicore CPUs demonstrate that our new implementation gives much better performance than two existing approaches in most cases.

关键词： CNNs Convolution FFT ARMv8 parallel algorithm

来源：评论

学校读者我要写书评

暂无评论

Structural Balance Computation in Signed networks by Using Multifactorial Discrete Particle Swarm Optimization

Structural Balance Computation in Signed Networks by Using M...

引用

IEEE international conference on Cloud computing and Intelligence Systems (CCIS)

作者： Changlong He Zengyang Shao Lijia Ma Jianqiang Li Tingyi Hu College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781665441506

the signed network has received widespread attention because it can well reflect the cooperation and conflict relationship. Structural balance is an important global feature in signed networks, which can well reflect the structural characteristics of the network. Existing structural balance calculation algorithms define the global and local balance computation problems as an optimization problem, and then optimize their respective objective functions through optimization algorithms, but these algorithms ignore the correlation between the two problems. In this paper, we combine the multifactorial evolutionary algorithm and the discrete particle swarm optimization algorithm, and further propose the multifactorial discrete particle swarm optimization algorithm (MFDPSO). this algorithm designs the knowledge transfer function and optimization algorithm based on the correlation of the strong and weak structure balance and optimizes the two problems at the same time. the experimental results on 8 real networks demonstrate the effectiveness of the MFDPSO.

关键词： Correlation parallel processing Search problems Linear programming Stability analysis Particle swarm optimization Task analysis

来源：评论

学校读者我要写书评

暂无评论

26th international European conference on parallel and Distributed computing, Euro-Par 2020

26th International European Conference on Parallel and Distr...

引用

26th international European conference on parallel and Distributed computing, Euro-Par 2020

ISBN: (纸本)9783030576745

the proceedings contain 39 papers. the special focus in this conference is on parallel and Distributed computing. the topics include: A makespan lower bound for the tiled cholesky factorization based on ALAP schedule;preface;skipping non-essential instructions makes data-dependence profiling faster;Optimal GPU-CPU offloading strategies for deep neural network training;improving mapping for sparse direct solvers: A trade-off between data locality and load balancing;modelling standard and randomized slimmed folded clos networks;ompMemOpt: Optimized memory movement for heterogeneous computing;Accelerating deep learning inference with cross-layer data reuse on GPUs;Distributed fine-grained traffic speed prediction for large-scale transportation networks based on automatic LSTM customization and sharing;Optimizing FFT-based convolution on ARMv8 multi-core CPUs;maximizing I/O bandwidth for reverse time migration on heterogeneous large-scale systems;TorqueDB: Distributed querying of time-series data from edge-local storage;data-centric distributed computing on networks of mobile devices;a toolchain to verify the parallelization of OmpSs-2 applications;WPSP: A multi-correlated weighted policy for VM selection and migration for cloud computing;LCP-aware parallel string sorting;Mobile RAM and shape formation by programmable particles;approximation algorithm for estimating distances in distributed virtual environments;on the power of randomization in distributed algorithms in dynamic networks with adaptive adversaries;3D coded SUMMA: Communication-efficient and robust parallel matrix multiplication;managing failures in task-based parallel workflows in distributed computing environments;accelerating nested data parallelism: Preserving regularity;using dynamic broadcasts to improve task-based runtime performances;a compression-based design for higher throughput in a lock-free hash map;NV-PhTM: An efficient phase-based transactional system for non-volatile memory.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Efficient Pattern Matching on CPU-GPU Heterogeneous Systems 19th

Efficient Pattern Matching on CPU-GPU Heterogeneous Systems

引用

19th international conference on Algorithms and Architectures for parallel Processing (ICA3PP)

作者： Sanz, Victoria Pousa, Adrian Naiouf, Marcelo De Giusti, Armando Natl Univ La Plata Sch Comp Sci III LIDI La Plata Argentina CIC Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn Buenos Aires DF Argentina

ISBN: (纸本)9783030389918;9783030389901

Pattern matching algorithms are used in several areas such as network security, bioinformatics and text mining, where the volume of data is growing rapidly. In order to provide real-time response for large inputs, high-performance computing should be considered. In this paper, we present a novel hybrid pattern matching algorithm that efficiently exploits the computing power of a heterogeneous system composed of multicore processors and multiple graphics processing units (GPUs). We evaluate the performance of our algorithm on a machine with 36 CPU cores and 2 GPUs and study its behaviour as the data size and the number of processing resources increase. Finally, we compare the performance of our proposal with that of two other algorithms that use only the CPU cores and only the GPUs of the system respectively. the results reveal that our proposal outperforms the other approaches for data sets of considerable size.

关键词： Pattern matching CPU-GPU computing CPU-GPU heterogeneous systems Hybrid programming Aho-Corasick

来源：评论

学校读者我要写书评

暂无评论

A Dependency-Aware Storage Schema Selection Mechanism for In-Memory Big Data computing Frameworks

引用

international JOURNAL OF parallel PROGRAMMING 2019年第3期47卷 502-519页

作者： Wang, Bo Tang, Jie Zhang, Rui Ding, Wei Qi, Deyu Beijing Inst Technol Beijing Peoples R China South China Univ Technol Guangzhou Guangdong Peoples R China Yanan Univ Yanan Peoples R China Henan Univ Technol Zhengzhou Henan Peoples R China

Artificial intelligence applications that greatly depend on deep learning and compute vision processing becomes popular. their strong demands for low-latency or real-time services make Spark, an in-memory big data computing framework, the best choice in taking place of previous disk-based big data computing. As an in-memory framework, reasonable data arrangement in storage is the key factor of performance. However, the existing cache replacement strategy and storage selection mechanism based optimizations all rely on an imprecise available memory model and will lead to negative decision. To address this issue, we propose an available memory model to capture the accurate information of to be freed memory space by sensing the dependencies between the data. And we also propose a maximum memory requirement model for execution prediction to exclude the redundancy from inactive blocks. With such two models, we build DASS, a dependency-aware storage selection mechanism for Spark to make dynamic and fine-grained storage decision. Our experiments show that compared with previous methods the DASS could effectively reduce the cost of garbage collection and RDD blocks re-computing, give better computing performance by 77.4%.

关键词： Big data In-memory computing frameworks Storage schema Performance optimization

来源：评论

学校读者我要写书评

暂无评论

16th international conference on ICT Innovations, ICT Innovations 2024

16th International Conference on ICT Innovations, ICT Innova...

引用

16th international conference on ICT Innovations, ICT Innovations 2024

ISBN: (纸本)9783031861611

the proceedings contain 21 papers. the special focus in this conference is on ICT Innovations. the topics include: Aligning Food Ingredients with Multiple Semantic Resources;crossword Generation as a Constraint Satisfaction Problem Using parallel Processing and Lemmatization;comprehensive Examination of network Access, Logging, and Auditing Strategies in Public and Private Institutions: Safeguarding Information Security, Resilience, and Compliance in the Digital Era;Benefits of parallelization in CPU Rendering: Quantitative Analysis Using a Custom 3D Rendering Engine;simulation of the Quasigroup Redundancy Check Code’s Ability to Detect Errors;YOLOv8 Oriented Bounding Box (OBB) Model for Waymo Open Dataset;deep Multimodal Fusion for Semantic Segmentation of Remote Sensing Earth Observation Data;transfer Learning with Yolo for Object Detection in Remote Sensing;comparison of On-Board and Off-Board Processing Power Consumption for Drone Camera Images;classification of Some Cosmological Images Using Deep Learning and Persistent Homology;mushroom Classification Using Machine Learning;towards a Framework for Promoting Student Engagement to Maximize Learning in Higher Education: A Case Study;Blood Oxygen Saturation Estimation Using PPG Signals from the MIMIC-III Database;novel Methodology for Gaining New Insights Into the Pharmacological Mechanisms of Cannabis sativa and Alzheimer’s Disease through Signaling Pathway Analysis Using Bioinformatics Tools;AI Cardiologist: Arrhythmia Detection by Transformer-Based Language Model;ambient Assisted Living Sensor-Based Solution for Elderly Self-monitoring;Classification of Autism and Typical Development Children Based on EEG Signals;detecting the Unseen: Exploiting Radar-Sonar Sensor Fusion for Visual Detection of Low-Profile Naval Drones;Evaluating Killer Drone Defense: NATO SPS Project "Anti-Drones" Field Trials.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Float-Fix: An Efficient and Hardware-Friendly Data Type for Deep Neural network

引用

international JOURNAL OF parallel PROGRAMMING 2019年第3期47卷 345-359页

作者： Han, Dong Zhou, Shengyuan Zhi, Tian Wang, Yibo Liu, Shaoli Chinese Acad Sci Intelligent Processor Res Ctr Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Cambricon Tech Ltd Beijing Peoples R China Tsinghua Univ Beijing Peoples R China

Recent years, as deep learning rose in prominence, neural network accelerators boomed. the existing research shows that both speed and energy-efficiency can be improved by low precision data structure. However, decreasing the precision of data might compromise the usefulness and accuracy of the underlying AI. And the existing studies can not meet all AI application requirements. In the paper, we propose a new data type, called Float-Fix (FF). We introduce the structure of FF and compare it with other data types. In our evaluation, the accuracy loss of 8-bit FF is less than 0.12% on a subset of known neural network models, 7x better than fixed-point, DFX and floating-point on average. We implement the hardware architectures of operators and neural processing unit using 8-bit FF data type with TSMC 65nm Gplus High VT library. the experiments show that the hardware cost of convertors converting between 16-bit fixed-point and FF is really small. And the multiplier of 8-bit FF only needs 1188m2 area, which is nearly 8-bit fixed-point. Comparing with the neural processing unit of DianNao, FF reduces 34.3% area.

关键词： Float-Fix Neural network Hardware accelerator Data type

来源：评论

学校读者我要写书评

暂无评论

An Ensemble Interpretable Machine Learning Scheme for Securing Data Quality at the Edge 4th

An Ensemble Interpretable Machine Learning Scheme for Securi...

引用

4th ifip TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 international Cross-Domain conference for Machine Learning and Knowledge Extraction, CD-MAKE 2020

作者： Karanika, Anna Oikonomou, Panagiotis Kolomvatsos, Kostas Anagnostopoulos, Christos Department of Informatics and Telecommunications University of Thessaly Volos Greece School of Computing Science University of Glasgow Glasgow United Kingdom

ISBN: (纸本)9783030573201

Data quality is a significant research subject for any application that requests for analytics to support decision making. It becomes very important when we focus on Internet of things (IoT) where numerous devices can interact to exchange and process data. IoT devices are connected to Edge computing (EC) nodes to report the collected data, thus, we have to secure data quality not only at the IoT infrastructure but also at the edge of the network. In this paper, we focus on the specific problem and propose the use of interpretable machine learning to deliver the features that are important to be based on for any data processing activity. Our aim is to secure data quality for those features, at least, that are detected as significant in the collected datasets. We have to notice that the selected features depict the highest correlation with the remaining ones in every dataset, thus, they can be adopted for dimensionality reduction. We focus on multiple methodologies for having interpretability in our learning models and adopt an ensemble scheme for the final decision. Our scheme is capable of timely retrieving the final result and efficiently selecting the appropriate features. We evaluate our model through extensive simulations and present numerical results. Our aim is to reveal its performance under various experimental scenarios that we create varying a set of parameters adopted in our mechanism. © 2020, ifip international Federation for Information Processing.

关键词： Internet of things

来源：评论

学校读者我要写书评

暂无评论

Improving the Performance of Distributed MXNet with RDMA

引用

international JOURNAL OF parallel PROGRAMMING 2019年第3期47卷 467-480页

作者： Li, Mingfan Wen, Ke Lin, Han Jin, Xu Wu, Zheng An, Hong Chi, Mengxian Univ Sci & Technol China Hefei 230026 Anhui Peoples R China

As one of the most influential deep learning frameworks, MXNet has achieved excellent performance and many breakthroughs in academic and industrial fields for various machine learning situations. the initial implementation of MXNet uses proxy-socket interface, which delivers suboptimal performance in distributed environment. In a massive parallel training task, parameters are updated frequently during each training loop, in which case network performance becomes the main factor of overall performance. Over the past decade, high performance interconnects have employed remote direct memory access (RDMA) technology to provide excellent performance for numerous scientific domains. In this paper, we describe an efficient design that extends the open-source MXNet to make it RDMA capable via RDMA-based parameter server interfaces. With modest optimizations towards memory usage and transmission overhead, RDMA-based MXNet achieves great performance improvement over the original software. Our experiments reveal that, for the communication subsystem of MXNet, the new design achieves 16x speedup (up to 21x at peak) over 1 Gigabit Ethernet (1GigE). For the two training cases on MXNet, the optimized implementation gains 5x and 9x speedup, respectively. Compared to experiments on the IP-over-InfiniBand (IPoIB) protocol, it achieves nearly 30% performance improvement, as well as better scalability and alleviation of bottlenecks.

关键词： Distributed MXNet Parameter server RDMA InfiniBand network optimization

来源：评论

学校读者我要写书评

暂无评论

Research on parallel Topology Analysis Algorithm Based on Simulated Annealing 8

Research on Parallel Topology Analysis Algorithm Based on Si...

引用

IEEE 8th Joint international Information Technology and Artificial Intelligence conference (ITAIC)

作者： Wang Xin-liang Chen Jian-Lin Ma Tian-Fang Liu Zhi-huai Liu Na Fang Wei Liu Xue-bin Zhang Li-Wei Wu Jun Hami Yuxin Energy Ind Res Inst Co Ltd Hami 839000 Peoples R China Henan Polytech Univ Sch Phys & Elect Informat Engn Jiaozuo 454000 Henan Peoples R China Hami Vocat & Tech Coll Hami 839000 Peoples R China

ISBN: (纸本)9781538681787

In the high-voltage power grid of coal mine, the existing adaptive topology analysis algorithm based on the correlation matrix for coal mine high-voltage power grid can effectively identify the network topology and construct a network topology analysis model to provide a basis for subsequent short-circuit current calculation and setting calculation. However, the topology analysis algorithm based on the correlation matrix takes much time. As the scale of the high-voltage power grid network of the coal mine increases, the time consumption will increase sharply as well. However, the prior-to-first-served parallel topology analysis algorithm has been used to reduce the topology analysis time consumption to some extent. On this basis, this article proposes a parallel topology analysis algorithm based on the simulated annealing and optimizes the scheduling strategy of parallel topology analysis. the simulation results show that compared with the prior-to-first-served parallel topology analysis algorithm, the parallel annealing topology analysis algorithm can further reduce the time consumption and improve the computational efficiency. And when the number of available threads increases, its computational efficiency will improve more significantly.

关键词： Topology analysis Simulated annealing High-voltage grid of coal mine parallel computing

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：