Výsledky vyhledávání - "Mathematics of computing Discrete mathematics Graph theory"

Lovász-Type Theorems and Game Comonads Autor Dawar, Anuj, Jakl, Tomas, Reggio, Luca

“…Lovász (1967) showed that two finite relational structures A and B are isomorphic if, and only if, the number of homomorphisms from C to A is the same as the…”
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The Logic of Graph Neural Networks Autor Grohe, Martin

“…Graph neural networks (GNNs) are deep learning architectures for machine learning problems on graphs. It has recently been shown that the expressiveness of…”
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HyperEF: Spectral Hypergraph Coarsening by Effective-Resistance Clustering Autor Aghdaei, Ali, Feng, Zhuo

“…This paper introduces a scalable algorithmic framework (HyperEF) for spectral coarsening (decomposition) of large-scale hypergraphs by exploiting hyperedge…”
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BlasPart: A Deterministic Parallel Partitioner for Balanced Large-Scale Hypergraph Partitioning Autor Tong, Shengbo, Pei, Chunyan, Yu, Wenjian

“…Balanced hypergraph partitioning is a fundamental problem in applications like VLSI design, high-performance computing, etc. Nowadays, large-scale hypergraphs…”
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On the Expressive Power of Homomorphism Counts Autor Atserias, Albert, Kolaitis, Phokion G., Wu, Wei-Lin

“…A classical result by Lovász asserts that two graphs G and H are isomorphic if and only if they have the same left profile, that is, for every graph F, the…”
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Graph Representation Learning for Microarchitecture Design Space Exploration Autor Yi, Xiaoling, Lu, Jialin, Xiong, Xiankui, Xu, Dong, Shang, Li, Yang, Fan

“…Design optimization of modern microprocessors is a complex task due to the exponential growth of the design space. This work presents GRL-DSE, an automatic…”
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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…”
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GraphFI: An Efficient Fault Injection Framework for Graph Processing on GPGPUs Autor Jiang, Nan, Yue, Hengshan, Tan, Jingweijia, Zhou, Mengting, Wang, Xiaonan, Wang, Yuchun, Wei, Wenda, Qiu, Meikang, Wei, Xiaohui

“…As graph tasks become pervasive in real-time and safetycritical domains (e.g., financial fraud detection and electrical power systems), it is also essential to…”
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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. Processing-in-memory (PIM) architecture based on the ReRAM…”
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TurboBC: A Memory Efficient and Scalable GPU Based Betweenness Centrality Algorithm in the Language of Linear Algebra Autor Artiles, Oswaldo, Saeed, Fahad

“…Betweenness centrality (BC) is a shortest path centrality metric used to measure the influence of individual vertices or edges on huge graphs that are used for…”
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A Synchronization-Avoiding Distance-1 Grundy Coloring Algorithm for Power-Law Graphs Autor Firoz, Jesun Sahariar, Zalewski, Marcin, Lumsdaine, Andrew

“…In this paper, we propose a distributed, unordered, label-correcting distance-1 Grundy (vertex) coloring algorithm, namely, Distributed Control (DC) coloring…”
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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…”
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A Scalable and Robust Compilation Framework for Emitter-Photonic Graph State Autor Ren, Xiangyu, Huang, Yuexun, Liang, Zhiding, Barbalace, Antonio

“…Quantum graph states are critical resources for various quantum algorithms, and also determine essential interconnections in distributed quantum computing…”
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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…”
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Automated Generation of Decoders for Irregular Instruction Sets Using Information-Theoretic Decision Tree Construction Algorithms Autor Tadros, Lillian

“…Instruction decoders are indispensable components of the System-on-Chip design flow and major constituents of instruction set simulators and processor…”
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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…”
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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…”
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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.

“…Electromigration (EM) becomes a major concern for VLSI circuits as the technology advances in the nanometer regime. With Korhonen equations, EM assessment for…”
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Lightweight Structural Choices Operator for Technology Mapping Autor Grosnit, Antoine, Zimmer, Matthieu, Tutunov, Rasul, Li, Xing, Chen, Lei, Yang, Fan, Yuan, Mingxuan, Bou-Ammar, Haitham

“…Technology mapping quality heavily depends on the subject graph structure. To overcome structural biases, operators construct choice nodes to enable mappings…”
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Consistent Graph Model Generation with Large Language Models Autor Chen, Boqi

“…Graph model generation from natural language requirements is an essential task in software engineering, for which large language models (LLMs) have become…”
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