Search Results - Theory of computation → Graph algorithms analysis

Skywalker: Efficient Alias-Method-Based Graph Sampling and Random Walk on GPUs by 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 by 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 by 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 by 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 by 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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SumPA: Efficient Pattern-Centric Graph Mining with Pattern Abstraction by 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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PIMGCN: A ReRAM-Based PIM Design for Graph Convolutional Network Acceleration by 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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NeuraChip: Accelerating GNN Computations with a Hash-based Decoupled Spatial Accelerator by Shivdikar, Kaustubh, Agostini, Nicolas Bohm, Jayaweera, Malith, Jonatan, Gilbert, Abellan, Jose L., Joshi, Ajay, Kim, John, Kaeli, David

“…Graph Neural Networks (GNNs) are emerging as a formidable tool for processing non-euclidean data across various domains, ranging from social network analysis to bioinformatic…”
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DyGNN: Algorithm and Architecture Support of Dynamic Pruning for Graph Neural Networks by 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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Parallelizing Maximal Clique Enumeration on GPUs by 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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iG-kway: Incremental k-way Graph Partitioning on GPU by 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 by 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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LearnGraph: A Learning-Based Architecture for Dynamic Graph Processing by 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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EMGraph: Fast Learning-Based Electromigration Analysis for Multi-Segment Interconnect Using Graph Convolution Networks by 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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Late Breaking Results: An Efficient and Scalable Track Assignment with GPU Parallelism by Liu, Genggeng, Huang, Pengcheng, Li, Zepeng, Liu, Wen-Hao, Huang, Xing, Guo, Wenzhong

“… Based on the independence and divisibility of track assignment, we propose a GPU-accelerated parallel track assignment algorithm…”
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Anchor First, Accelerate Next: Revolutionizing GNNs with PIM by Harnessing Stationary Data by 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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NEO-DNND: Communication-Optimized Distributed Nearest Neighbor Graph Construction by Iwabuchi, Keita, Steil, Trevor, Priest, Benjamin W., Pearce, Roger, Sanders, Geoffrey

“…Graph-based approximate nearest neighbor algorithms have shown high neighbor structure representation quality…”
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SGIRR: Sparse Graph Index Remapping for ReRAM Crossbar Operation Unit and Power Optimization by 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 by 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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Dynamic Allocation of Processor Cores to Graph Applications on Commodity Servers by 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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