检索结果-内蒙古大学图书馆

51st International conference on parallel processing (ICPP)

作者： Li, Shengwei Lai, Zhiquan Li, Dongsheng Zhang, Yiming Ye, Xiangyu Duan, Yabo Univ Def Technol Natl Key Lab Parallel & Distributed Proc Comp Coll Changsha Peoples R China Xiamen Univ Xiamen Peoples R China

ISBN: (纸本)9781450397339

distributed data-parallel training has been widely adopted for deep neural network (DNN) models. Although current deep learning (DL) frameworks scale well for dense models like image classification models, we find that these DL frameworks have relatively low scalability for sparse models like natural language processing (NLP) models that have highly sparse embedding tables. Most existing works overlook the sparsity of model parameters thus suffering from significant but unnecessary communication overhead. In this paper, we propose EmbRace, an efficient communication framework to accelerate communications of distributed training for sparse models. EmbRace introduces Sparsity-aware Hybrid Communication, which integrates AlltoAll and model parallelism into data-parallel training, so as to reduce the communication overhead of highly sparse parameters. To effectively overlap sparse communication with both backward and forward computation, EmbRace further designs a 2D Communication Scheduling approach which optimizes the model computation procedure, relaxes the dependency of embeddings, and schedules the sparse communications of each embedding row with a priority queue. We have implemented a prototype of EmbRace based on PyTorch and Horovod, and conducted comprehensive evaluations with four representative NLP models. Experimental results show that EmbRace achieves up to 2.41x speedup compared to the state-of-the-art distributed training baselines.

关键词： distributed training deep learning sparsity of NLP models communication scheduling

来源：评论

学校读者我要写书评

暂无评论

Computing the Euclidean distance transform on a linear array of processors

引用

JOURNAL OF SUPERCOMPUTING 2003年第2期25卷 177-185页

作者： Gavrilova, ML Alsuwaiyel, MH Univ Calgary Dept Comp Sci Calgary AB T2N 1N4 Canada KFUPM Dept Informat & Comp Sci Dhahran Saudi Arabia

Given an n x n binary image of white and black pixels, we present an optimal parallel algorithm for computing the distance transform and the nearest feature transform using the Euclidean metric. The algorithm employs ... 详细信息

关键词： feature transform distance transform Euclidean distance parallel algorithm linear array of processors image processing

来源：评论

学校读者我要写书评

暂无评论

Domain Adaptive Semantic Segmentation via image Translation and Representation Alignment 19

Domain Adaptive Semantic Segmentation via Image Translation ...

引用

19th IEEE International Symposium on parallel and distributed processing with Applications (IEEE ISPA)

作者： Kang, Jingxuan Zang, Bin Cao, Weipeng Univ Liverpool Sch Comp Sci Liverpool Merseyside England Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9781665435741

Domain Adaptation for semantic segmentation is of vital significance since it enables effective knowledge transfer from a labeled source domain (i.e., synthetic data) to an unlabeled target domain (i.e., real images), where no effort is devoted to annotating target samples. Prior domain adaptation methods are mainly based on image-to-image translation model to minimize differences in image conditions between source and target domain. However, there is no guarantee that feature representations from different classes in the target domain can be well separated, resulting in poor discriminative representation. In this paper, we propose a unified learning pipeline, called image Translation and Representation Alignment (ITRA), for domain adaptation of segmentation. Specifically, it firstly aligns an image in the source domain with a reference image in the target domain using image style transfer technique (e.g., CycleGAN) and then a novel pixelcentroid triplet loss is designed to explicitly minimize the intraclass feature variance as well as maximize the inter-class feature margin. When the style transfer is finished by the former step, the latter one is easy to learn and further decreases the domain shift. Extensive experiments demonstrate that the proposed pipeline facilitates both image translation and representation alignment and significantly outperforms previous methods in both GTA5 -> Cityscapes and SYNTHIA -> Cityscapes scenarios.

关键词： Domain Adaptation Semantic Segmentation Style Transfer Centroid Alignment

来源：评论

学校读者我要写书评

暂无评论

Towards a Hierarchical Exascale Framework for Iterative parallel Data Analysis Algorithms 32

Towards a Hierarchical Exascale Framework for Iterative Para...

引用

32nd Euromicro International conference on parallel, distributed and Network-Based processing (PDP)

作者： Cesario, Eugenio Lindia, Paolo Univ Calabria Arcavacata Di Rende CS Italy

ISBN: (纸本)9798350363074;9798350363081

Several parallel and distributed data mining algorithms have been proposed in literature to perform large scale data analysis, overcoming the bottleneck of traditional methods on a single machine. However, although the master-worker approach greatly simplifies the synchronization of all nodes since only the master is in charge to do that, it also presents several problematic issues for large-scale data analysis tasks (involving thousands or millions of nodes). This paper presents a hierarchical (or multi-level) master-worker framework for iterative parallel data analysis algorithms, to overcome the scalability issues affecting classic master-worker solutions. Specifically, the framework is composed of (more than one) merger and worker nodes organized in a k-tree structure, in which the workers are on the leaves and the mergers are on the root and the internal nodes in the tree.

关键词： distributed data mining exascale computing high performance computing

来源：评论

学校读者我要写书评

暂无评论

parallel Implementation of Segmentation Algorithms on Graphics processing Unit

Parallel Implementation of Segmentation Algorithms on Graphi...

引用

21st Signal processing and Communications Applications conference (SIU)

作者： Yenialp, Erdal Kalkan, Habil Suleyman Demirel Univ Bilgisayar Muhendisligi Bolumu Vis Image Proc & Pattern Recognit Lab VIPLAB TR-32200 Isparta Turkey

ISBN: (纸本)9781467355636;9781467355629

Segmentation algorithms are widely used in image processing. These methods have different complexity values and the choice of reasonable methods decreases on large images. Especially on the medical images with large size, it may take days to perform segmentation in some methods. However, parallel implementation may eliminate the drawback of these algorithms to some extent. In this study, we propose to implement segmentation algorithms in parallel using Graphical processing Unit. Using the proposed implementation, the computation time of the K-centers, K-means and DBSCAN algorithms were decreases 87, 642 and 2 times, respectively.

关键词： parallel programming cuda dbscan k-means k-centers segmentation

来源：评论

学校读者我要写书评

暂无评论

Experiments with parallelizing a tribology application

Experiments with parallelizing a tribology application

引用

31st International conference on parallel processing (ICPP 2002)

作者： Chaudhary, V Hase, WL Jiang, H Sun, L Thakert, D Wayne State Univ Inst Comp Sci Detroit MI 48202 USA

ISBN: (纸本)0769516807

Different parallelization methods vary in their system requirements, programming styles, efficiency of exploring parallelism, and the application characteristics they can handle. Different applications can exhibit totally different performance gains depending on the parallelization method used. This paper compares OpenMP, MPI, and Strings(A distributed shared memory)for parallelizing a complicated tribology problem. The problem size and computing infrastructure are changed and their impacts on the parallelization methods are studied. All of the methods studied exhibit good performance improvements. This paper exhibits the benefits that are the result of applying parallelization techniques to applications in this field.

关键词： molecular dynamics OpenMP MPI distributed shared memory

来源：评论

学校读者我要写书评

暂无评论

A Raster Data Framework Based on distributed Heterogeneous Cluster

引用

JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2019年第4期47卷 715-723页

作者： Bhushan, R. Phani Somayajulu, D. V. L. N. Venkatraman, S. Subramanyam, R. B. V. ADRIN Dept Space Hyderabad Telangana India NIT Dept Comp Sci & Engn Warangal Andhra Pradesh India

Advancements in satellite imaging and sensor technologies result in capturing of large amount of spatial data. Many parallel processing techniques based on data or control parallelism have been attempted during the past 2 decades to provide performance improvement in image processing applications such as urban sprawl, weather prediction and crop estimation. These techniques have used block-based distributed file processing or the more modern MapReduce-based programming for implementation which still have gaps between optimal and best processing in terms of resource scheduling, data distribution and ease of programming. In this paper, we present a layered framework for parallel data processing to improve storage, retrieval and processing performance of spatial data on an underlying distributed file system. The paper presents a data placement strategy across a distributed HDFS cluster in a way to optimize spatial data retrieval and processing. The presence of neighborhood pixels local to the processing node in a distributed environment reduces network latencies and improves the efficiency of applications such as object recognition, change detection and site selection. We evaluate the data placement strategy on a four-node HDFS cluster and show that it can deliver good performance benefits by way of reading blocks of data at almost 10-12 times the default, which contributes to the improvement in efficiency of the various applications that use region growing methods.

关键词： Big data Remote sensing Spatial MapReduce HDFS RecordReader Raster data Vector data Resource scheduler Retrieval Engine

来源：评论

学校读者我要写书评

暂无评论

Trellis-SDP: A simple data-parallel programming interface

Trellis-SDP: A simple data-parallel programming interface

引用

33rd International conference on parallel processing

作者： Ding, M Lu, P Univ Alberta Dept Comp Sci Edmonton AB T6G 2E8 Canada

ISBN: (纸本)0769521983

Some datasets and computing environments are inherently distributed. For example, image data may be gathered and stored at different locations. Although data parallelism is a well-known computational model, there are few programming systems that are both easy to program (for simple applications) and can work across administrative domains. We have designed and implemented a simple programming system, called Trellis-SDP, that facilitates the rapid development of data-intensive applications. Trellis-SDP is layered on top of the Trellis infrastructure, a software system for creating overlay metacomputers: user-level aggregations of computer systems. Trellis-SDP provides a master-worker programming framework where the worker components can run self-contained, new or existing binary applications. We describe two interface functions, namely trellis_scano() and trellis_gather(), and show how easy it is to get reasonable performance with simple data-parallel applications, such as Content Based image Retrieval (CBIR) and parallel Sorting by Regular Sampling (PSRS).

关键词： parallel processing systems

来源：评论

学校读者我要写书评

暂无评论

Exploiting Julia for parallel RBF-Based 3D Surface Reconstruction: A First Experience 32

Exploiting Julia for Parallel RBF-Based 3D Surface Reconstru...

引用

32nd Euromicro International conference on parallel, distributed and Network-Based processing (PDP)

作者： De Luca, Pasquale Galletti, Ardelio Marcellino, Livia Pianese, Mario Parthenope Univ Naples Sci & Technol Dept Naples Italy

ISBN: (纸本)9798350363074;9798350363081

The 3D surface reconstruction is critical for various applications, demanding efficient computational approaches. Traditional Radial Basis Functions (RBFs) methods are limited by increasing data points, leading to slower execution times. Addressing this, our study introduces an experimental parallelization effort using Julia, as well-known for high-performance scientific computing. We developed an initial sequential RBF algorithm in Julia, then expanded it to a parallel model, exploiting Multi-Threading to enhance execution speed while maintaining accuracy. This initial exploration into Julia's parallel computing capabilities shows marked performance gains in 3D surface reconstruction, offering promising directions for future research. Our findings affirm Julia's potential in computationally intensive tasks, with test results confirming the expected time efficiency improvements.

关键词： surface reconstruction radial basis functions parallel algorithms Julia programming

来源：评论

学校读者我要写书评

暂无评论

INFORMATION-ENTROPY BASED LOAD BALANCING IN parallel ADAPTIVE VOLUME RENDERING

INFORMATION-ENTROPY BASED LOAD BALANCING IN PARALLEL ADAPTIV...

引用

International conference on Interfaces and Human Computer Interaction / International conference on Game and Entertainment Technologies / International conference on Computer Graphics, Visualization, Computer Vision and image processing

作者： Wang, Huawei Ai, Zhiwei Cao, Yi Inst Appl Phys & Computat Math 2 Fenghao East Rd Beijing 100094 Peoples R China

ISBN: (纸本)9789898533388

Aiming at TB-scale time-varying scientific datasets, this paper presents a novel static load balancing scheme based on information entropy to enhance the efficiency of the parallel adaptive volume rendering algorithm. An information-theory model is proposed firstly, and then the information entropy is calculated for each data patch, which is taken as a pre-estimation of the computational amount of ray sampling. According to their computational amounts, the data patches are distributed to the processing cores balancedly, and accordingly load imbalance in parallel rendering is decreased. Compared with the existing methods such as random assignment and ray estimation, the proposed entropy-based load balancing scheme can achieve a rendering speedup ratio of 1.23 similar to 2.84. It is the best choice in interactive volume rendering due to its speedup performance and view independence.

关键词： Load Balancing Information Entropy parallel Volume Rendering View Independence

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：