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检索条件"任意字段=10th International Conference on Algorithms and Architectures for Parallel Processing"

共 2747 条记录，以下是321-330 订阅

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CHEAPS2AGA: Bounding Space Usage in Variance-Reduced Stochastic Gradient Descent over Streaming Data and Its Asynchronous parallel Variants 20th

CHEAPS2AGA: Bounding Space Usage in Variance-Reduced Stochas...

引用

20th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Peng, Yaqiong Fei, Haiqiang Li, Lun Ding, Zhenquan Hao, Zhiyu Chinese Acad Sci Inst Informat Engn Beijing 100093 Peoples R China

ISBN: (纸本)9783030602390;9783030602383

Stochastic Gradient Descent (SGD) is widely used to train a machine learning model over large datasets, yet its slow convergence rate can be a bottleneck. As a remarkable family of variance reduction techniques, memory algorithms such as SAG and SAGA have been proposed to accelerate the convergence rate of SGD. However, these algorithms need to store per training data point corrections in memory. the unlimited space usage feature is impractical for modern large-scale applications, especially over data points that arrive over time (referred to as streaming data in this paper). To overcome this weakness, this paper investigates the methods that bound the space usage in the state-of-the-art family of variance-reduced stochastic gradient descent over streaming data, and presents CHEAPS2AGA. At each step of updating the model, the key idea of CHEAPS2AGA is always reserving N random data points as samples, while re-using information about past stochastic gradients across all the observed data points with limited space usage. In addition, training an accurate model over streaming data requires the algorithm to be time-efficient. To accelerate the model training phase, CHEAPS2AGA embraces a lock-free data structure to insert new data points and remove unused data points in parallel, and updates the model parameters without using any locking. We conduct comprehensive experiments to compare CHEAPS2AGA to prior related algorithms suited for streaming data. the experimental results demonstrate the practical competitiveness of CHEAPS2AGA in terms of scalability and accuracy.

关键词： Machine learning Stochastic gradient descent Streaming data parallel algorithm Multicore

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Inline Double Layer Depth Estimation with Transparent Materials 42nd

Inline Double Layer Depth Estimation with Transparent Materi...

引用

42nd German conference on Pattern Recognition, DAGM GCPR 2020 held in parallel with 25th international Symposium on Vision, Modeling, and Visualization, VMV 2020 and 10th Eurographics Workshop on Visual Computing for Biology and Medicine, VCBM 2020

作者： Kopf, Christian Pock, thomas Blaschitz, Bernhard Štolc, Svorad Graz University of Technology Graz Austria AIT Austrian Institute of Technology GmbH Vienna Austria Photoneo s.r.o. Bratislava Slovakia

ISBN: (纸本)9783030712778

3D depth computation from stereo data has been one of the most researched topics in computer vision. While state-of-art approaches have flourished over time, reconstruction of transparent materials is still considered an open problem. Based on 3D light field data we propose a method to obtain smooth and consistent double-layer estimates of scenes with transparent materials. Our novel approach robustly combines estimates from models with different layer hypotheses in a cost volume with subsequent minimization of a joint second order TGV energy on two depth layers. Additionally we showcase the results of our approach on objects from common inspection use-cases in an industrial setting and compare our work to related methods. © 2021, Springer Nature Switzerland AG.

关键词： Stereo image processing

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Performance Analysis of Continuous Binary Data processing using Distributed Databases within Stream processing Environments 10

Performance Analysis of Continuous Binary Data Processing us...

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10th international conference on Cloud Computing and Services Science (CLOSER)

作者： Weissbach, Manuel Hilbert, Hannes Springer, thomas Tech Univ Dresden Fac Comp Sci Dresden Germany

ISBN: (纸本)9789897584244

Big data applications must process increasingly large amounts of data within ever shorter time. Often a stream processing engine (SPE) is used to process incoming data with minimal latency. While these engines are designed to process data quickly, they are not made to persist and manage it. thus, databases are still integrated into streaming architectures, which often becomes a performance bottleneck. To overcome this issue and achieve maximum performance, all system components used must be examined in terms of their throughput and latency, and how well they interact with each other. Several authors have already analyzed the performance of popular distributed database systems. While doing so, we focus on the interaction between the SPEs and the databases, as we assume that stream processing leads to changes in the access patterns to the databases. Moreover, our main focus is on the efficient storing and loading of binary data objects rather than typed data, since in our use cases the actual data analysis is not to be performed by the database, but by the SPE. We've benchmarked common databases within streaming environments to determine which software combination is best suited for these requirements. Our results show that the database performance differs significantly depending on the access pattern used and that different software combinations lead to substantial performance differences. Depending on the access pattern, Cassandra, MongoDB and PostgreSQL achieved the best throughputs, which were mostly the highest when Apache Flink was used.

关键词： Stream processing Benchmarking Database Benchmark Big Data Performance

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Automatic Placement of Tasks to NUMA Nodes in Iterative Applications 28

Automatic Placement of Tasks to NUMA Nodes in Iterative Appl...

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28th Euromicro international conference on parallel, Distributed and Network-Based processing (PDP)

作者： Dokulil, Jiri Benkner, Siegfried Univ Vienna Fac Comp Sci Vienna Austria

ISBN: (纸本)9781728165820

Manycore architectures with non-uniform memory access (NUMA) are commonly used 14 high-performance computing. On these systems, the placement of data and computation to the NUMA nodes has very significant impact on performance, especially with memory-bound applications. this placement can usually be defined by the programmer, but it is generally desirable to automate the placement to simplify the programmer's job, improve portability, and make the code more future-proof. Task-based runtime systems already assume a fair degree of responsibility for task placement, so it is only natural to involve them in mapping work and data to NUMA nodes. In this work, we propose a solution where the runtime system first performs a profiling run of the application and measures various performance characteristics. then, the data collected by the profiling run is used by a stand-alone analyzer to create a plan for placing tasks to NUMA nodes so that the tasks are close to the data that they most rely on. this plan is then used by the runtime system to execute the application more efficiently. We focus on iterative applications, where the same patterns of tasks are being repeated. We identify these patterns and use them to create a plan that works for any number of iterations, not just the one that used when the application was observed. In our experiments, which were performed on modern manycore systems (Intel Skylake and AML) Zen) with 4 and 8 NUMA nodes, the proposed automated placement can either match or come close (<10%) to a hand-tuned placement.

关键词： parallel tasks NUMA task scheduling

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Pimiento: A Vertex-Centric Graph-processing Framework on a Single Machine 19th

Pimiento: A Vertex-Centric Graph-Processing Framework on a S...

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19th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Huang, Jianqiang Qin, Wei Wang, Xiaoying Chen, Wenguang Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China Qinghai Univ Dept Comp Technol & Applicat Xining 810016 Peoples R China

ISBN: (纸本)9783030389611;9783030389604

Here, we describe a method for handling large graphs with data sizes exceeding memory capacity using minimal hardware resources. this method (called Pimiento) is a vertex-centric graph-processing framework on a single machine and represents a semi-external graph-computing system, where all vertices are stored in memory, and all edges are stored externally in compressed sparse row data-storage format. Pimiento uses a multi-core CPU, memory, and multi-threaded data preprocessing to optimize disk I/O in order to reduce random-access overhead in the graph-algorithm implementation process. An on-the-fly update-accumulated mechanism was designed to reduce the time that the graph algorithm accesses disks during execution. Our experiments compared external this method with other graph-processing systems, including GraphChi, X-Stream, and FlashGraph, revealing that Pimiento achieved 7.5x, 4x, 1.6x better performance on large real-world graphs and synthetic graphs in the same experimental environment.

关键词： Vertex-centric Graph processing Semi-external Passing message Asynchronous update accumulation

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Proceedings - 2020 IEEE international Symposium on parallel and Distributed processing with Applications, 2020 IEEE international conference on Big Data and Cloud Computing, 2020 IEEE international Symposium on Social Computing and Networking and 2020 IEEE international conference on Sustainable Computing and Communications, ISPA-BDCloud-SocialCom-SustainCom 2020

Proceedings - 2020 IEEE International Symposium on Parallel ...

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18th IEEE international Symposium on parallel and Distributed processing with Applications, 10th IEEE international conference on Big Data and Cloud Computing, 13th IEEE international Symposium on Social Computing and Networking and 10th IEEE international conference on Sustainable Computing and Communications, ISPA-BDCloud-SocialCom-SustainCom 2020

ISBN: (纸本)9781665414852

the proceedings contain 202 papers. the topics discussed include: multi-relational graph convolutional networks for skeleton-based action recognition;identifying the right person in social networks with double metaphone codes;a novel strong cache consistency mechanism in ICN based on role division and lease model;demystifying the largest live game streaming platform via black-box measurement;framework-agnostic optimization of repeated skewed joins at massive scale;remote procedure call optimization of big data systems based on data awareness;MCSEC: secure coded matrix multiplication scheme for edge computing with minimum communication cost;low-rate DoS attack detection based on WPD-EE algorithm;user identity linkage across location-based social networks with spatio-temporal check-in patterns;optimizing 3-D placement of multiple UAVs based on Taguchiâ€™s method;and feature envy detection based on Bi-LSTM with self-attention mechanism.

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parallel SCC Detection Based on Reusing Warps and Coloring Partitions on GPUs 20th

Parallel SCC Detection Based on Reusing Warps and Coloring P...

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20th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Hou, Junteng Wang, Shupeng Wu, Guangjun Ma, Bingnan Zhang, Lei Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Natl Comp Network Emergency Response Tech Team Coordinat Ctr China Beijing Peoples R China

ISBN: (纸本)9783030602451;9783030602444

Detecting strongly connected components (SCCs) in directed graphs is crucial for analyzing the structure of graphs. It can accelerate the process of large-scale graphs with applying GPUs to graph computation. However, GPU-based SCC detection still faces the challenge from graphs, especially real-world graphs, skewing and strict synchronization requirement. In this paper, we proposed a new paradigm of identifying SCCs with reusing warps and coloring partitions. the scheme of reusing warps assigns multiple vertices to each virtual warp at one time, which greatly reduces the number of warps that are applied but assigned few tasks. Furthermore, we proved that the partitions formed by coloring independently contains all SCCs in them, so we use coloring to identify more partitions and parallel detect SCCs in each partition to get more SCCs. Finally, the colored partitions are deeply combined with the following traversal to optimize the algorithm. We conduct extensive theoretical and experimental analysis to demonstrate the efficiency and accuracy of our approach. the experimental results expose that our approach can achieve 7.23x, 30.55x, 1.75x and 1.26x speedup for SCC detection using NVIDIA K80 compared with algorithms of Tarjan, Barnat, Hong and Slota respectively.

关键词： Strongly connected components detection GPUs Reusing warps Coloring partitions Partitions combined with traversal

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CenterlineNet: Automatic Coronary Artery Centerline Extraction for Computed Tomographic Angiographic Images Using Convolutional Neural Network architectures 10

CenterlineNet: Automatic Coronary Artery Centerline Extracti...

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10th international conference on Image processing theory, Tools and Applications (IPTA)

作者： Rjiba, Saifeddine Urruty, thierry Bourdon, Pascal Fernandez-Maloigne, Christine Delepaule, Roger Christiaens, Luc-Philippe Guillevin, Remy Canon Med Syst France Puteaux La Defense France Univ Poitiers Poitiers France Univ Poitiers Hosp Poitiers France Univ & Hosp Poitiers Poitiers France

ISBN: (纸本)9781728187501

the prevention of cardiovascular diseases starts by a thorough examination of the coronary artery vessels for atherosclerotic plaques existence. By combining deep learning convolutional Neural Network (CNN) architectures and the biological knowledge, we introduce a novel method for the automatic extraction of coronary artery centerlines in Computed Tomography Angiography (CTA) data. the proposed method is based on a 3D convolutional neural network used as a local vessel centerline detector to extract the main and side branches of the coronary artery tree. Coupled with a preprocessing vessel enhancing step, the model takes advantage of the analytical analysis of the former as well as the feature extraction ability of the CNN. In addition, automatic aorta and heart region detections are also performed to delineate the origin and the extent of the coronary tree and phase out the false predictions. We investigate the use of Coronary Stenosis' Detection and Quantification (2012) Database to enrich the training process while ensuring the homogeneity of the scans. Our work offers a fully automatic, CNN based, pipeline for coronary artery centerline detection while circumventing the scarcity of the medical data in hand. Its validation against the ground truth annotation from the Rotterdam Coronary Artery Algorithm Evaluation Framework yields a retrieval ratio of 95% and 93%.

关键词： Convolutional neural networks

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Nahuatl Neural Machine Translation Using Attention Based architectures: A Comparative Analysis for RNNs and Transformers as a Mobile Application Service 20th

Nahuatl Neural Machine Translation Using Attention Based Arc...

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20th Mexican international conference on Artificial Intelligence (MICAI)

作者： Bello Garcia, Sergio Khalil Sanchez Lucero, Eduardo Bonilla Huerta, Edmundo Hernandez Hernandez, Jose Crispin Ramirez Cruz, Jose Federico Pedroza Mendez, Blanca Estela Tecnol Nacl Mexico Inst Tecnol Apizaco Tzompantepec 90300 Tlaxcala Mexico

ISBN: (纸本)9783030898205;9783030898199

Machine Translation is a problem that consists of automating the task of translating a sentence into another target language done by a computer, and is still in research, especially with low-resource languages. the neoteric introduction of attention techniques inside the Natural Language processing (NLP) field in coalescence with a broader disposal of word-segmentation and Web Scrapping techniques;including the lack of a proper online tool translation for Nahuatl dialect, inspired this work in an effort to produce such a tool. Once availability of suitable corpus via Web Scrapping is searched for with scrutiny, therefore, doubling the state of the art in parallel phrases;several vocabulary files were produced using two sorts of word segmentation tools in order to extract the morphemes and break down the agglutination Nahuatl contains. By performing a comparative analysis between Recurrent Neural Networks (RNNs) and Transformers, incorporating two segmentation techniques and two different corpus, it is possible to improve the state of the art regarding Nahuatl by more than four times the BLEU score (66.45) with second validation by using a Fuzzy similarity library. Such experiments confirmed the hypothesis that by increasing the corpus size by double, using transformers and sub-word segmentation, a translation from Spanish to Nahuatl is the best approach that can be accomplished so far with the current tools;outperforming many times Statistical Machine Translation (SMT) and RNNs which do not contain attention, plus the deployment of an application that serves as a platform for the language.

关键词： Neural Machine Translation Nahuatl Transformers Recurrent Neural Networks Mobile application

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Accelerating Deep Learning using Multiple GPUs and FPGA-Based 10GbE Switch 28

Accelerating Deep Learning using Multiple GPUs and FPGA-Base...

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28th Euromicro international conference on parallel, Distributed and Network-Based processing (PDP)

作者： Itsubo, Tomoya Koibuchi, Michihiro Amano, Hideharu Matsutani, Hiroki Keio Univ Kohoku Ku 3-14-1 Hiyoshi Yokohama Kanagawa 2238522 Japan Natl Inst Informat Chiyoda Ku 2-1-2 Hitotsubashi Tokyo 1018430 Japan

ISBN: (数字)9781728165820

ISBN: (纸本)9781728165820

A back-propagation algorithm following a gradient descent approach is used for training deep neural networks. Since it iteratively performs a large number of matrix operations to compute the gradients, GPUs (Graphics processing Units) are efficient especially for the training phase. thus, a cluster of computers each of which equips multiple GPUs can significantly accelerate the training phase. Although the gradient computation is still a major bottleneck of the training, gradient aggregation and parameter optimization impose both communication and computation overheads, which should also be reduced for further shortening the training time. To address this issue, in this paper, multiple GPUs are interconnected with a PCI Express (PCIe) over 10Gbit Ethernet (10GbE) technology. Since these remote GPUs are interconnected via network switches, gradient aggregation and optimizers (e.g., SGD, Adagrad, Adam, and SMORMS3) are offloaded to an FPGA-based network switch between a host machine and remote GPUs;thus, the gradient aggregation and optimization are completed in the network. Evaluation results using four remote GPUs connected via the FPGA-based 10GbE switch that implements the four optimizers demonstrate that these optimization algorithms are accelerated by up to 3.0x and 1.25x compared to CPU and GPU implementations, respectively. Also, the gradient aggregation throughput by the FPGA-based switch achieves 98.3% of the 10GbE line rate.

关键词： Optimization Switches Training Graphics processing units Acceleration Machine learning Computational modeling

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