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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

共 10372 条记录，以下是401-410 订阅

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Enabling Efficient NVM-Based Text Analytics without Decompression 40

Enabling Efficient NVM-Based Text Analytics without Decompre...

引用

40th IEEE International conference on Data Engineering, ICDE 2024

作者： Fang, Xiaokun Zhang, Feng Nong, Junxiang Zhang, Mingxing Hu, Puyun Chai, Yunpeng Du, Xiaoyong School of Information Renmin University of China China Tsinghua University Department of Computer Science and Engineering China

ISBN: (纸本)9798350317152

Text analytics directly on compression (TADOC) is a promising technology designed for handling big data analytics. However, a substantial amount of DRAM is required for high performance, which limits its usage in many important scenarios where the capacity of DRAM is limited, such as memory-constrained systems. Non-volatile memory (NVM) is a novel storage technology that combines the advantage of reading per-formance and byte addressability of DRAM with the durability of traditional storage devices like SSD and HDD. Unfortunately, no research demonstrates how to use NVM to reduce DRAM utilization in compressed data analytics. In this paper, we propose N-TADOC, which substitutes DRAM with NVM while maintaining TADOC's analytics performance and space savings. Utilizing an NVM block device to reduce DRAM utilization presents two challenges, including poor data locality in traversing datasets and auxiliary data structure reconstruction on NVM. We develop novel designs to solve these challenges, including a pruning method with NVM pool management, bottom-up upper bound estimation, correspondent data structures, and persistence strategy at different levels of cost. Experimental results show that on four real-world datasets, N-TADOC achieves 2.04× performance speedup compared to the processing directly on the uncompressed data and 70.7% DRAM space saving compared to the original TADOC. © 2024 IEEE.

关键词： Dynamic random access storage

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SAFDNet: A Simple and Effective Network for Fully Sparse 3D Object Detection

SAFDNet: A Simple and Effective Network for Fully Sparse 3D ...

引用

IEEE/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Zhang, Gang Chen, Junnan Gao, Guohuan Li, Jianmin Liu, Si Hu, Xiaolin Tsinghua Univ Inst AI Dept Comp Sci & Technol BNRist Beijing Peoples R China Huazhong Univ Sci & Technol Wuhan Peoples R China Beijing Inst Technol Beijing Peoples R China Beihang Univ Inst Artificial Intelligence Beijing Peoples R China Tsinghua Lab Brain & Intelligence THBI Beijing Peoples R China Chinese Inst Brain Res CIBR Beijing 100010 Peoples R China

ISBN: (纸本)9798350353006

LiDAR-based 3D object detection plays an essential role in autonomous driving. Existing high-performing 3D object detectors usually build dense feature maps in the backbone network and prediction head. However, the computational costs introduced by the dense feature maps grow quadratically as the perception range increases, making these models hard to scale up to long-range detection. Some recent works have attempted to construct fully sparse detectors to solve this issue;nevertheless, the resulting models either rely on a complex multi-stage pipeline or exhibit inferior performance. In this work, we propose a fully sparse adaptive feature diffusion network (SAFDNet) for LiDAR-based 3D object detection. In SAFDNet, an adaptive feature diffusion strategy is designed to address the center feature missing problem. We conducted extensive experiments on Waymo Open, nuScenes, and Argoverse2 datasets. SAFDNet performed slightly better than the previous SOTA on the first two datasets but much better on the last dataset, which features long-range detection, verifying the efficacy of SAFDNet in scenarios where long-range detection is required. Notably, on Argoverse2, SAFDNet surpassed the previous best hybrid detector HEDNet by 2.6% mAP while being 2.1x faster, and yielded 2.1% mAP gains over the previous best sparse detector FSDv2 while being 1.3x faster. The code will be available at https://***/zhanggang001/HEDNet.

关键词： 3D Object Detection computer Vision Feature Diffusion Fully Sparse Detector

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SIMULATION OF DYNAMIC WEIGHTED MS DECODING ALGORITHM ON LDPC FOR 5G 19

SIMULATION OF DYNAMIC WEIGHTED MS DECODING ALGORITHM ON LDPC...

引用

19th IEEE International computer conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Wang Qianchun Caballero, Jonathan M. Huainan Normal Univ Sch Elect Engn Huainan Anhui Peoples R China Technol Univ Philippines Dept Grad Program Manila Philippines

ISBN: (纸本)9781665493895

According to the characteristics of high-speed, low delay, large-capacity data transmission and various scenarios in 5G mobile communication, an improved decoding algorithm based on LDPC code for 5G is proposed. The algorithm adopts dynamic weighting decoding method to dynamically weight the information. In this way, the iteration can be accelerated when the information is transmitted, to reduce the number of iterations and improve the gain at the same time. From the point of view that 5G channel coding needs to support more bit rates, the simulation results show that compared with the traditional decoding algorithm, the performance of the improved MS algorithm is improved by about 0.4dB, and the dynamic weighting method improves the decoding performance and reduces the number of decoding iterations.

关键词： 5G LDPC code Algorithm Increase gain

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AdpDM: Adaptive Data Model for Efficient Dynamic Management of Large-Scale high-Cardinality Time-Series Databases 29th

AdpDM: Adaptive Data Model for Efficient Dynamic Management ...

引用

29th International conference on Database Systems for Advanced Applications (DASFAA)

作者： Xu, Ziyue Huang, Sutong Fei, Di Yi, Liping Zhou, Chenfei Wang, Gang Liu, Xiaoguang Liu, Xinyu Yu, Wenqing Wei, Zijing Liu, Shaozhi Nankai Univ Coll Comp Sci GTIISC TMCCSysNetDISSec Tianjin Peoples R China Alibaba Grp Holding Ltd Hangzhou Peoples R China

ISBN: (纸本)9789819755684;9789819755691

With advancements in Internet of Things (IoT) and performance monitoring, time-series database have garnered significant attention. Time-series consists of two components: series keys and data points. Prior efforts focus on reducing the storage cost of data points. However, as the scale of data grows rapidly, storing series keys and their indexes consumes at most 75% storage costs, still leading to unacceptably high query latency. Furthermore, the growing cardinalityof time-series also slows down the construction of real-time indexes. Previous methods often rely on a fixed data model or user-selected hot data mechanism for indexing, resulting in a small number of accessed indexes and high query latency. To pursue low storage costs, rapid real-time index construction and efficient query processing simultaneously, we propose a novel Adaptive Data Model (AdpDM) for large-scale high-cardinality time-series data management. It involves three core modules: 1) a grouping algorithm based on the similarity of series keys is designed to group time-series;2) a group-level inverted index instead of the traditional series-level inverted index is constructed to reduce storage costs and query latency;3) for real-time group-level index construction, a grouping strategy reuse mechanism is proposed to improve ingestion throughput. We conduct extensive experiments to compare AdpDM with other advanced time-series databases, the results present that AdpDM reduces 80% storage cost of indexes, and 90% query latency while achieving 2.8x higher writing throughput.

关键词： Time-series database Data management Indexing

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HeterSim: Enable Flexible Simulation for Heterogeneous Distributed Deep Learning Platform

HeterSim: Enable Flexible Simulation for Heterogeneous Distr...

引用

2024 International conference on Mechatronics and Intelligent Control, ICMIC 2024

作者： Tang, Yinan Zhang, Hongwei Wang, Li Guo, Zhenhua Zhao, Yaqian Li, Rengang IEIT SYSTEM Co. Ltd. Beijing China

ISBN: (数字)9781510686847

ISBN: (纸本)9781510686830

Distributed deep learning has become an essential technique for accelerating deep learning, but its performance is often influenced by the heterogeneous computing nodes and heterogeneous communication networks within the distributed deep learning platform. Due to the high costs of practical deployment and running of distributed deep learning, it is almost impossible for researchers to optimize the training strategy of distributed deep learning tasks in real-world environments. In this paper, we propose HeterSim, a simulator specifically designed for heterogeneous distributed deep learning platforms. HeterSim enables flexible configuration of computing node performance and network connections, and supports to define and simulate distributed deep learning workloads using graph-based representations. HeterSim also allows for the modification of communication strategies in the distributed deep learning process, thereby assisting researchers in validating their designs for distributed deep learning. We verify the feasibility and flexibility of HeterSim, by generating a simulation platform at the scale of millions of nodes, and successfully simulate the distributed deep learning process of Resnet50. We aim to provide HeterSim as a flexible and user-friendly simulator for researchers, targeting heterogeneous distributed deep learning platforms, and helping researchers evaluate and optimize the strategy of distributed deep learning tasks at a lower cost. © 2025 SPIE.

关键词： computer simulations Switches Deep learning Education and training Data modeling Modeling Computation time computer architecture Manufacturing Mathematical modeling

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One-Shot high-Fidelity Talking-Head Synthesis with Deformable Neural Radiance Field

One-Shot High-Fidelity Talking-Head Synthesis with Deformabl...

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IEEE/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Li, Weichuang Zhang, Longhao Wang, Dong Zhao, Bin Wang, Zhigang Chen, Mulin Zhang, Bang Wang, Zhongjian Bo, Liefeng Li, Xuelong Shanghai AI Lab Shanghai Peoples R China Alibaba Grp DAMO Acad Hangzhou Peoples R China Northwestern Polytech Univ Xian Shaanxi Peoples R China

ISBN: (纸本)9798350301298

Talking head generation aims to generate faces that maintain the identity information of the source image and imitate the motion of the driving image. Most pioneering methods rely primarily on 2D representations and thus will inevitably suffer from face distortion when large head rotations are encountered. Recent works instead employ explicit 3D structural representations or implicit neural rendering to improve performance under large pose changes. Nevertheless, the fidelity of identity and expression is not so desirable, especially for novel-view synthesis. In this paper, we propose HiDe-NeRF, which achieves high-fidelity and free-view talking-head synthesis. Drawing on the recently proposed Deformable Neural Radiance Fields, HiDe-NeRF represents the 3D dynamic scene into a canonical appearance field and an implicit deformation field, where the former comprises the canonical source face and the latter models the driving pose and expression. In particular, we improve fidelity from two aspects: (i) to enhance identity expressiveness, we design a generalized appearance module that leverages multi-scale volume features to preserve face shape and details;(ii) to improve expression preciseness, we propose a lightweight deformation module that explicitly decouples the pose and expression to enable precise expression modeling. Extensive experiments demonstrate that our proposed approach can generate better results than previous works. Project page: https://***/hidenerf/

关键词： Image and video synthesis and generation

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Easily Implementable Large-Area Multi-Band Stealth Structure 15

Easily Implementable Large-Area Multi-Band Stealth Structure

引用

15th International conference on Information Optics and Photonics, CIOP 2024

作者： Rong, Yitian He, Tongtong Li, Jinxin Ou, Chunhui College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9781510686175

ISBN: (纸本)9781510686168

The structure of most current multi-band stealth materials is very complex, making large-scale production and application difficult. We have demonstrated a structure that not only achieves multi-band stealth functionality but is also easy to produce and apply on a large scale. Contrast to previous approaches used in broadband range, this design exhibits a selective narrowband high absorption in the microwave range, marking a departure from conventional methods. The metasurface structure consists of an infrared shielding layer (ISL) and a microwave absorption layer (MAL), which respectively reduce the emissivity in the infrared band and the reflectivity in specific microwave bands. Simulation experimental data shows that the structure has excellent multi band stealth performance. © 2024 SPIE.

关键词： Electromagnetic shielding

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SCL-GCD of Short Polar Codes

SCL-GCD of Short Polar Codes

引用

2024 IEEE Global Communications conference, GLOBECOM 2024

作者： Zheng, Xiangping Wang, Qianfan Ma, Xiao Sun Yat-sen University School of Computer Science and Engineering Guangzhou510006 China

ISBN: (纸本)9798350351255

This paper is concerned with the SCL-GCD algorithm of polar codes, which performs the successive-cancellation list (SCL) decoding algorithm for a lower rate sub-code and the guessing codeword decoding (GCD) algorithm for a higher rate sub-code. We propose to implement the GCD algorithm in a parallel way and design early stopping criteria for reducing complexity and decoding latency without sacrificing performance. Numerical results show that, when compared with the original SCL decoding algorithm, the SCL-GCD algorithm armed with the proposed early stopping criteria has lower computational complexity and decoding latency in the high signal-noise ratio (SNR) region. © 2024 IEEE.

关键词： Signal to noise ratio

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A PID-Incorporated Second-Order Latent Factor Analysis Model 21

A PID-Incorporated Second-Order Latent Factor Analysis Model

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21st International conference on Networking, Sensing and Control, ICNSC 2024

作者： Wang, Jialiang Xia, Yan Yuan, Ye College of Computer and Information Science Southwest University Chongqing China School of Electronic Information and Communication Engineering Chongqing Aerospace Polytechnic Chongqing China

ISBN: (纸本)9798350365221

A second-order-based latent factor (SLF) analysis model demonstrates superior performance in graph representation learning, particularly for, high-dimensional and incomplete (HDI) interaction data, by incorporating the curvature information of the loss landscape. However, its objective function is commonly bi-linear and non-convex, causing the SLF model to suffer from a low convergence rate. To address this issue, this paper proposes a PID controller-incorporated SLF (PSLF) model, leveraging two key strategies: a) refining learning error estimation by incorporating the PID controller principles, and b) acquiring second-order information insights through Hessian-vector products. Experimental results on multiple industry HDI datasets indicate that the proposed PSLF model outperforms four state-of-the-art latent factor models based on advanced optimizers regarding convergence rates and generalization performance. © 2024 IEEE.

关键词： Three term control systems

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FT K-Means: A high-performance K-Means on GPU with Fault Tolerance

FT K-Means: A High-Performance K-Means on GPU with Fault Tol...

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2024 IEEE International conference on Cluster Computing, CLUSTER 2024

作者： Wu, Shixun Ding, Yitong Zhai, Yujia Liu, Jinyang Huang, Jiajun Jian, Zizhe Dai, Huangliang Di, Sheng Wong, Bryan M. Chen, Zizhong Cappello, Franck University of California RiversideCA United States University of Houston HoustonTX United States Argonne National Laboratory LemontIL United States

ISBN: (纸本)9798350358711

K-means is a widely used algorithm in clustering, how-ever, its efficiency is primarily constrained by the computational cost of distance computing. Existing implementations suffer from suboptimal utilization of computational units and lack resilience against soft errors. To address these challenges, we introduce FT K-means, a high-performance GPU-accelerated implementation of K-means with online fault tolerance. We first present a step-wise optimization strategy that achieves competitive performance compared to NVIDIA's cuML library. We further improve FT K-means with a template-based code generation framework that supports different data types and adapts to different input shapes. A novel warp-level tensor-core error correction scheme is proposed to address the failure of existing fault tolerance methods due to mem-ory asynchronization during copy operations. Our experimental evaluations on NVIDIA T4 GPU and A100 GPU demonstrate that FT K-means without fault tolerance outperforms cuML's K-means implementation, showing a performance increase of 10%-300% in scenarios involving irregular data shapes. Moreover, the fault tolerance feature of FT K-means introduces only an overhead of 11%, maintaining robust performance even with tens of errors injected per second. © 2024 IEEE.

关键词： computer graphics equipment

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