Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Balin, Muhammed Fatih"'
Autor:
Sancak, Kaan, Hua, Zhigang, Fang, Jin, Xie, Yan, Malevich, Andrey, Long, Bo, Balin, Muhammed Fatih, Çatalyürek, Ümit V.
Graph Neural Networks (GNNs) have shown impressive performance in graph representation learning, but they face challenges in capturing long-range dependencies due to their limited expressive power. To address this, Graph Transformers (GTs) were intro
Externí odkaz:
http://arxiv.org/abs/2406.12059
Training large scale Graph Neural Networks (GNNs) requires significant computational resources, and the process is highly data-intensive. One of the most effective ways to reduce resource requirements is minibatch training coupled with graph sampling
Externí odkaz:
http://arxiv.org/abs/2310.12403
Partitioning for load balancing is a crucial first step to parallelize any type of computation. In this work, we propose SGORP, a new spatial partitioning method based on Subgradient Optimization, to solve the $d$-dimensional Rectilinear Partitioning
Externí odkaz:
http://arxiv.org/abs/2310.02470
Graph Neural Networks (GNNs) have received significant attention recently, but training them at a large scale remains a challenge. Mini-batch training coupled with sampling is used to alleviate this challenge. However, existing approaches either suff
Externí odkaz:
http://arxiv.org/abs/2210.13339
Full batch training of Graph Convolutional Network (GCN) models is not feasible on a single GPU for large graphs containing tens of millions of vertices or more. Recent work has shown that, for the graphs used in the machine learning community, commu
Externí odkaz:
http://arxiv.org/abs/2110.08688
Even distribution of irregular workload to processing units is crucial for efficient parallelization in many applications. In this work, we are concerned with a spatial partitioning called rectilinear partitioning (also known as generalized block dis
Externí odkaz:
http://arxiv.org/abs/2009.07735