Výsledky vyhľadávania - Theory of computation → Dynamic graph algorithms

Skywalker: Efficient Alias-Method-Based Graph Sampling and Random Walk on GPUs Autor Wang, Pengyu, Li, Chao, Wang, Jing, Wang, Taolei, Zhang, Lu, Leng, Jingwen, Chen, Quan, Guo, Minyi

“…Graph sampling and random walk operations, capturing the structural properties of graphs, are playing an important role today as we cannot directly adopt computing-intensive algorithms on large-scale graphs…”
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ParGNN: A Scalable Graph Neural Network Training Framework on multi-GPUs Autor Gu, Junyu, Li, Shunde, Cao, Rongqiang, Wang, Jue, Wang, Zijian, Liang, Zhiqiang, Liu, Fang, Li, Shigang, Zhou, Chunbao, Wang, Yangang, Chi, Xuebin

“… over-partition to alleviate load imbalance. Based on the over-partition results, we present a subgraph pipeline algorithm to overlap communication and computation while maintaining the accuracy of GNN training…”
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BlasPart: A Deterministic Parallel Partitioner for Balanced Large-Scale Hypergraph Partitioning Autor Tong, Shengbo, Pei, Chunyan, Yu, Wenjian

“… Thus, fast and high-quality deterministic partitioning algorithms are largely in demand…”
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ACGraph: Accelerating Streaming Graph Processing via Dependence Hierarchy Autor Jiang, Zihan, Mao, Fubing, Guo, Yapu, Liu, Xu, Liu, Haikun, Liao, Xiaofei, Jin, Hai, Zhang, Wei

“…Streaming graph processing needs to timely evaluate continuous queries. Prior systems suffer from massive redundant computations due to the irregular order of processing vertices influenced by updates…”
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DS-GL: Advancing Graph Learning via Harnessing Nature's Power within Scalable Dynamical Systems Autor Song, Ruibing, Wu, Chunshu, Liu, Chuan, Li, Ang, Huang, Michael, Geng, Tony Tong

“… problems and have been adopted for traditional graph computation, such as max-cut. However, when performing complex Graph Learning (GL…”
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DyGNN: Algorithm and Architecture Support of Dynamic Pruning for Graph Neural Networks Autor Chen, Cen, Li, Kenli, Zou, Xiaofeng, Li, Yangfan

“…Recently, graph neural networks (GNNs) have achieved great success for graph representation learning tasks…”
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LearnGraph: A Learning-Based Architecture for Dynamic Graph Processing Autor Zhang, Lingling, Wu, Yijian, Jiang, Hong, Zhou, Ziyu, Lu, Tiancheng

“…Dynamic graph processing systems using conventional array-based architectures face significant throughput limitations due to inefficient memory access and index management…”
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SumPA: Efficient Pattern-Centric Graph Mining with Pattern Abstraction Autor Gui, Chuangyi, Liao, Xiaofei, Zheng, Long, Yao, Pengcheng, Wang, Qinggang, Jin, Hai

“…Graph mining aims to explore interesting structural information of a graph. Pattern-centric systems typically transform a generic-purpose graph mining problem into a series of subgraph matching problems for high performance…”
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Parallelizing Maximal Clique Enumeration on GPUs Autor Almasri, Mohammad, Chang, Yen-Hsiang, Hajj, Izzat El, Nagi, Rakesh, Xiong, Jinjun, Hwu, Wen-mei

“…We present a GPU solution for exact maximal clique enumeration (MCE) that performs a search tree traversal following the Bron-Kerbosch algorithm…”
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PIMGCN: A ReRAM-Based PIM Design for Graph Convolutional Network Acceleration Autor Yang, Tao, Li, Dongyue, Han, Yibo, Zhao, Yilong, Liu, Fangxin, Liang, Xiaoyao, He, Zhezhi, Jiang, Li

“…Graph Convolutional Network (GCN) is a promising but computing- and memory-intensive learning model…”
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iG-kway: Incremental k-way Graph Partitioning on GPU Autor Lee, Wan Luan, Jiang, Shui, Lin, Dian-Lun, Chang, Che, Zhang, Boyang, Chung, Yi-Hua, Schlichtmann, Ulf, Ho, Tsung-Yi, Huang, Tsung-Wei

“…Recent advances in GPU-accelerated graph partitioning have achieved significant performance gains but remain limited to full graph partitioning, lacking support for incremental updates…”
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GPart: A GNN-Enabled Multilevel Graph Partitioner Autor Chen, Magi, Wang, Ting-Chi

“…This paper introduces GPart, a scalable multilevel framework for graph partitioning that integrates GNN embeddings with efficient coarsening and refinement techniques…”
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EMGraph: Fast Learning-Based Electromigration Analysis for Multi-Segment Interconnect Using Graph Convolution Networks Autor Jin, Wentian, Chen, Liang, Sadiqbatcha, Sheriff, Peng, Shaoyi, Tan, Sheldon X.-D.

“… VLSI multisegment interconnect trees can be naturally viewed as graphs. Based on this observation, we propose a new graph convolution network (GCN…”
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Dynamic Allocation of Processor Cores to Graph Applications on Commodity Servers Autor Pons, Lucia, Sahuauillo, Julio, Jones, Timothy M.

“…Graph processing is increasingly adopted to solve problems that span many application domains, including scientific computing, social networks, and big-data analytics…”
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Anchor First, Accelerate Next: Revolutionizing GNNs with PIM by Harnessing Stationary Data Autor Chen, Jiaxian, Qi, Yuxuan, Zhu, Yongbiao, Yuan, Jianan, Sun, Kaoyi, Wang, Tianyu, Ma, Chenlin, Wang, Yi

“…Substantial data movement caused by irregular graph topologies hinders the efficient processing of graph neural networks (GNNs…”
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SGIRR: Sparse Graph Index Remapping for ReRAM Crossbar Operation Unit and Power Optimization Autor Wang, Cheng-Yuan, Chang, Yao-Wen, Chang, Yuan-Hao

“…Resistive Random Access Memory (ReRAM) Crossbars are a promising process-in-memory technology to reduce enormous data movement overheads of large-scale graph processing between computation and memory units…”
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PSMiner: A Pattern-Aware Accelerator for High-Performance Streaming Graph Pattern Mining Autor Qi, Hao, Zhang, Yu, He, Ligang, Luo, Kang, Huang, Jun, Lu, Haoyu, Zhao, Jin, Jin, Hai

“…Streaming Graph Pattern Mining (GPM) has been widely used in many application fields…”
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A Synchronization-Avoiding Distance-1 Grundy Coloring Algorithm for Power-Law Graphs Autor Firoz, Jesun Sahariar, Zalewski, Marcin, Lumsdaine, Andrew

“… We implement our DC coloring algorithm and the well-known Jones-Plassmann algorithm and compare their performance with 4 different types of standard RMAT graphs and real-world graphs…”
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SAGA: A Memory-Efficient Accelerator for GANN Construction via Harnessing Vertex Similarity Autor Chen, Ruiyang, Liu, Xueyuan, Qi, Chunyu, Yao, Yuanzheng, Sun, Yanan, Liang, Xiaoyao, Song, Zhuoran

“… Although architectures like NDSearch have been proposed to accelerate GANN search, they are hard to deploy for GANN construction, as their pre-processing methods introduce massive overhead in dynamic graphs…”
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PairGraph: An Efficient Search-space-aware Accelerator for High-performance Concurrent Pairwise Queries Autor Fu, Yutao, Long, Zhongtian, Zhang, Yu, He, Zirui, Zhao, Jin, Niu, Qiyuan, Wang, Zixiao, Jin, Hai

“…) because of the poor temporal and spatial locality of traversal overlaps (i.e., graph structure data traversed by several queries…”
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