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

共 10259 条记录，以下是91-100 订阅

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ConsistDreamer: 3D-Consistent 2D Diffusion for high-Fidelity Scene Editing

ConsistDreamer: 3D-Consistent 2D Diffusion for High-Fidelity...

引用

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

作者： Chen, Jun-Kun Buio, Samuel Rota Muller, Norman Porzi, Lorenzo Kontschieder, Peter Wang, Yu-Xiong Univ Illinois Urbana IL 61820 USA Meta Menlo Pk CA USA

ISBN: (纸本)9798350353006

This paper proposes ConsistDreamer - a novel frame-work that lifts 2D diffusion models with 3D awareness and 3D consistency, thus enabling high-fidelity instruction-guided scene editing. To overcome the fundamental limitation of missing 3D consistency in 2D diffusion models, our key insight is to introduce three synergistic strategies that augment the input of the 2D diffusion model to become 3D-aware and to explicitly enforce 3D consistency during the training process. Specifically, we design surrounding views as context-rich input for the 2D diffusion model, and generate 3D-consistent structured noise instead of image-independent noise. Moreover, we introduce self-supervised consistency-enforcing training within the per-scene editing procedure. Extensive evaluation shows that our ConsistDreamer achieves state-of-the-art performance for instruction-guided scene editing across various scenes and editing instructions, particularly in complicated large-scale indoor scenes from ScanNet++, with significantly improved sharpness and fine-grained textures. Notably, ConsistDreamer stands as the first work capable of success-fully editing complex (e.g., plaid/checkered) patterns. Our project page is at ***/ConsistDreamer.

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Application of MAC Protocol Reinforcement Learning in Wireless Network Environment 15

Application of MAC Protocol Reinforcement Learning in Wirele...

引用

15th International conference on Information and Communication Technology Convergence, ICTC 2024

作者： Park, Junyoung Na, Woongsoo Kongju National University Department of Computer Science and Engineering Cheonan Korea Republic of Kongju National University Department of Software Cheonan Korea Republic of

ISBN: (纸本)9798350364637

This paper has introduced studies on applying reinforcement learning to MAC protocols in wireless networks to enhance network performance. As services and users demanding improved network performance increase, using reinforcement learning in MAC protocols can significantly enhance overall network performance. Continued research on MAC protocols based on preceding studies could lead to the development of protocols that reduce power consumption while achieving low latency and high throughput. © 2024 IEEE.

关键词： Adversarial machine learning

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high-efficiency tandem red organic light-emitting diodes adopting exciplex hosts 11

High-efficiency tandem red organic light-emitting diodes ado...

引用

2024 11th International conference on Advanced Manufacturing Technology and Materials Engineering, AMTME 2024

作者： Wang, Jiong Zhang, Shengdong School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China

Deep-red organic light-emitting diodes (OLEDs) exhibit significant potential for applications in infrared medical treatment and infrared imaging. However, the OLED performance drops significantly with increasing emission wavelength due to the energy gap law. To overcome this challenge, an efficient interfacial exciplex host, NPB/TPBi, has been developed. Simulation results indicate that the interfacial exciplex host demonstrates superior performance, compared with the bulk exciplex host and single host. To further boost the performance of red OLEDs, the interfacial exciplex host was incorporated into tandem devices, achieving an external quantum efficiency of up to 27.3%. This work offers important insights for the fabrication of efficient red OLEDs. © 2024 Institute of Physics Publishing. All rights reserved.

关键词： Organic light emitting diodes (OLED)

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PEANUTS: A Persistent Memory-Based Network Unilateral Transfer System for Enhanced MPI-IO Data Transfer 30th

PEANUTS: A Persistent Memory-Based Network Unilateral Transf...

引用

30th European conference on Parallel and Distributed Processing (Euro-Par)

作者： Hiraga, Kohei Tatebe, Osamu Univ Tsukuba Tsukuba Ibaraki Japan

ISBN: (纸本)9783031697654;9783031697661

This paper introduces PEANUTS, a system designed to improve I/O performance for Single Shared File (SSF) in high-performance computing scenarios. I/O performance evaluation of SSF on 100 nodes demonstrated write speeds of 2.47 TB/s, remote read speeds of 2.39 TB/s, and local read speeds of 7.75 TB/s. These outcomes are close to the hardware's performance limits and represent significant improvements of approximately 2.2 times, 2.3 times, and 7.5 times, respectively, compared to existing state-of-the-art systems. A major feature of PEANUTS is the integration of persistent memory with RDMA one-sided communication, supporting high-speed and low-latency data transfers without the need for separate storage servers on the compute nodes. This configuration allows all compute node CPU cores to be fully available for application processing. Seamless integration with the MPI runtime enables rapid data sharing through the MPI-IO interface. The advancements by PEANUTS suggest that utilizing large-scale SSF could solidify as the standard I/O framework in HPC, demonstrating a viable solution to overcome traditional performance limitations.

关键词： Persistent Memory One-Sided Communication Storage System MPI-IO Single Shared File high-performance Computing

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Durin: CPU-FPGA Heterogeneous Platform for Scalable Low-Dimensional Data Clustering

Durin: CPU-FPGA Heterogeneous Platform for Scalable Low-Dime...

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2024 IEEE International conference on Big Data, BigData 2024

作者： Lim, Se-Min Aliaj, Esmerald Jun, Sang-Woo University of California Irvine Department of Computer Science IrvineCA United States

ISBN: (纸本)9798350362480

Reconfigurable hardware accelerators, known for their high performance and power efficiency, have yet to be fully leveraged for clustering low-dimensional data at realistic scales. In this work, we identify and address two hurdles for accelerating this important class of applications: the overhead of implementing indexing data structures in hardware, and the PCIe bottleneck when the data capacity spills over from accelerator memory to host storage. We overcome these hurdles for the first time with Durin, a CPU-FPGA heterogeneous system with a hardware-software codesigned index structure, which minimizes the hardware resource overhead of high-performance neighbor search. It also minimizes PCIe overhead by facilitating asynchronous acceleration of distance calculation, and block floating-point compression. We show that a desktop computer with Durin implemented on a mid-range Alveo U50 FPGA can outperform a 32-thread Xeon server by almost 20× , with an order of magnitude power efficiency improvements. Furthermore, Durin outperforms even the best-case projection of a conventional standalone accelerator design, which implements the entirety of the clustering algorithm in the FPGA and its high Bandwidth Memory (HBM), by 2×. © 2024 IEEE.

关键词： Personal computers

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AP2PNET: Attention P2PNET for Sparse 3D Leg Reconstruction 21

AP2PNET: Attention P2PNET for Sparse 3D Leg Reconstruction

引用

21st International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Hang, Liu School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798331519254

Although the current point cloud upsampling techniques are already capable of attaining arbitrary upsampling rates af-ter one training. However, during the upsampling process, it may be difficult to preserve the fine geometric details in the original point cloud, resulting in insufficient detail in the up-sampled results and poor performance on our collected leg da-taset. Moreover, the network structure of this method is relatively complex, with high computational complexity and in-creased training costs. To tackle the issues mentioned above, we propose a new attention enhanced multi-scale feature extraction method. First, we extract local features through multilayer perceptron, and then extract important channel and spatial feature information through attention mechanisms to obtain multi-scale feature representations for learning point-to-point distances. Compared with the Grad-PU method, our method reduces the size of network parameters by 54.41%, experi-mental results demonstrate that our approach achieves the state-of-the-art upsampling performance. © 2024 IEEE.

关键词： Deep learning

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BiEPNet: Bilateral Edge-perceiving Network for high-Resolution Human Parsing 24

BiEPNet: Bilateral Edge-perceiving Network for High-Resoluti...

引用

8th International conference on Digital Signal Processing (ICDSP)

作者： Gong, Qiqi Wei, Yunchao Zhao, Yao Beijing Jiaotong Univ Inst Informat Sci Beijing Key Lab Adv Informat Sci & Network Beijing Peoples R China

ISBN: (纸本)9798400709029

Human parsing is a fundamental task aimed at segmenting human images into distinct body parts and holds vast potential applications. Nowadays, the advancement of image-capturing devices has led to a growing number of high-resolution human images. Receptive field, detail loss and memory usage are a triplet of contradictions in high-resolution scenarios. Existing human parsing methods designed for low-resolution inputs struggle to process high-resolution images efficiently due to their massive demands for computation and memory. Some methods save resources by overwhelmingly downsampling or encoding high-resolution inputs at the cost of poor performance on details. To resolve the issues above, we propose the Bilateral Edge-Perceiving Network (BiEPNet), consisting of a resources-friendly semantic-perceiving branch to acquire sufficient global information and a simple yet effective edge-perceiving branch used to refine details. The attention mechanism is utilized to simultaneously enhance the perception of context and details, leading to better performance on the boundary regions. To verify the effectiveness of BiEPNet, we contribute a high-resolution human parsing dataset, Human4K, containing 4,000 images with more than five million pixels. Extensive experiments on Human4K demonstrate that our method effectively outperforms the state-of-the-art methods.

关键词： BiEPNet Human parsing high resolution computer vision

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Heterogeneous many-core optimization method for PipeBCGs solver in PETSc on SW26010-pro 3

Heterogeneous many-core optimization method for PipeBCGs sol...

引用

3rd International conference on Electronic Information Engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Wang, Xinhua Pan, Jingshan Tian, Min Xu, Chaoshuai Liu, Huazeng Wei, Zhenguo Qilu University of Technology Shandong Academy of Sciences Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (纸本)9781510680449

The PETSc (Portable, Extensible Toolkit for Scientific Computation) library is one of the fundamental general-purpose numerical libraries in high-performance computing environments. It is widely employed for solving problems related to partial differential equations, sparse linear algebra, and other related issues. PETSc plays a crucial role in assisting developers in rapidly creating parallel programs, thereby enhancing the computational efficiency of high-performance computing. This paper initially ports the PETSc program onto the SW26010-pro processor. Following that, we choose a representative solver from the KSP module of PETSc. Addressing four hotspot functions invoked by this solver, we implement many-core optimization for its execution under the Sunway heterogeneous architecture. The experimental results show that the maximum speedup on a single node can reach 62.3 after many-core optimizations of core hotspot functions. This indicates that the many-core optimization of hotspot functions for PETSc linear solvers demonstrates excellent parallel efficiency on the Sunway supercomputer. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Computational efficiency

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Choosing the Best Parallelization and Implementation Styles for Graph Analytics Codes: Lessons Learned from 1106 programs 23

Choosing the Best Parallelization and Implementation Styles ...

引用

2023 International conference for high performance Computing, Networking, Storage and Analysis, SC 2023

作者： Liu, Yiqian Azami, Noushin Vanausdal, Avery Burtscher, Martin Department of Computer Science Texas State University San Marcos United States

ISBN: (纸本)9798400701092

Graph analytics has become a major workload in recent years. The underlying core algorithms tend to be irregular and data dependent, making them challenging to parallelize. Yet, these algorithms can be implemented and parallelized in many ways for CPUs and even more ways for GPUs. We took 6 key graph algorithms and created hundreds of CUDA, OpenMP, and parallel C++ versions of each of them, most of which have never been described or studied. To determine which parallelization and implementation styles work well and under what circumstances, we evaluated the resulting 1106 programs on 2 GPUs and 2 CPUs using 5 input graphs. Our results show which styles and combinations perform well and which ones should be avoided. We found that choosing the wrong implementation style can yield over a 10× slowdown on average. The worst combinations of styles can cost 6 orders of magnitude in performance. © 2023 ACM.

关键词： program processors

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Re-VoxelDet: Rethinking Neck and Head Architectures for high-performance Voxel-based 3D Detection

Re-VoxelDet: Rethinking Neck and Head Architectures for High...

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IEEE/CVF Winter conference on Applications of computer Vision (WACV)

作者： Lee, Jae-Keun Lee, Jin-Hee Lee, Joohyun Kwon, Soon Jung, Heechul DGIST Daegu South Korea FutureDrive Inc Seoul South Korea Kyungpook Natl Univ Daegu South Korea

ISBN: (纸本)9798350318920;9798350318937

LiDAR-based 3D object detectors usually adopt grid-based approaches to handle sparse point clouds efficiently. However, during this process, the down-sampled features inevitably lose spatial information, which can hinder the detectors from accurately predicting the location and size of objects. To address this issue, previous researches proposed sophisticatedly designed neck and head modules to effectively compensate for information loss. Inspired by the core insights of previous studies, we propose a novel voxel-based 3D object detector, named as Re-VoxelDet, which combines three distinct components to achieve both good detection capability and real-time performance. First, in order to learn features from diverse perspectives without additional computational costs during inference, we introduce Multi-view Voxel Backbone (MVBackbone). Second, to effectively compensate for abundant spatial and strong semantic information, we design Hierarchical Voxel-guided Auxiliary Neck (HVANeck), which attentively integrates hierarchically generated voxel-wise features with RPN blocks. Third, we present Rotation-based Group Head (RGHead), a simple yet effective head module that is designed with two groups according to the heading direction and aspect ratio of the objects. Through extensive experiments on the Argoverse2, Waymo Open Dataset and nuScenes, we demonstrate the effectiveness of our approach. Our results significantly outperform existing state-of-the-art methods. We plan to release our model and code(1) in the near future.

关键词： Applications Applications Autonomous Driving Robotics

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