Graph Neural Networks have demonstrated remarkable effectiveness in various graph-based tasks, but their inefficiency in training and inference poses significant challenges for scaling to real-world, large-scale appli...
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Graph Neural Networks have demonstrated remarkable effectiveness in various graph-based tasks, but their inefficiency in training and inference poses significant challenges for scaling to real-world, large-scale applications. To address these challenges, a plethora of algorithms have been developed to accelerate GNN training and inference, garnering substantial interest from the research community. This paper presents a systematic review of these acceleration algorithms, categorizing them into three main topics: training acceleration, inference acceleration, and execution acceleration. For training acceleration, we discuss techniques like graph sampling and GNN simplification. In inference acceleration, we focus on knowledge distillation, GNN quantization, and GNN pruning. For execution acceleration, we explore GNN binarization and graph condensation. Additionally, we review several libraries related to GNN acceleration, including our Scalable Graph Learning library, and propose future research directions.
Different summation acceleration techniques exist that help in solving large scale problems where complex series appear. Most of these acceleration techniques have been implemented in algorithms for planar problems;ho...
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ISBN:
(纸本)9788890701832;9781467321877
Different summation acceleration techniques exist that help in solving large scale problems where complex series appear. Most of these acceleration techniques have been implemented in algorithms for planar problems;however it is also possible to implement some of these techniques in the series summations appearing in the analysis of conformal antennas. Two adequate summation acceleration techniques are investigated and modified in order to achieve better convergence. The obtained results show excellent convergence properties and applying this method to conformal antenna problem shows that significant reductions in computational time and memory usage can be achieved.
Nowadays, medical image processing and three-dimensional visualization have become a very important support for doctor diagnosis and treatment planning. It's a novel technology that using ITK and VTK to process an...
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ISBN:
(纸本)9783037855744
Nowadays, medical image processing and three-dimensional visualization have become a very important support for doctor diagnosis and treatment planning. It's a novel technology that using ITK and VTK to process and display medical images in VC++. In this paper, the Curvature FlowImageFilter class of ITK libraries is used to denoise and smooth the medical images. The MC algorithms is used to reconstruct the volume data in the VS2008. Immediate Mode algorithms and Stripper filter algorithms are adopted to speed up large data processing. The experiment results demonstrate that using the MC algorithms and the acceleration algorithms base on the VTK can implement 3-D visualization efficiently and satisfy practical applications.
Different summation acceleration techniques exist that help in solving large scale problems where complex series appear. Most of these acceleration techniques have been implemented in algorithms for planar problems;ho...
详细信息
ISBN:
(纸本)9781467321877
Different summation acceleration techniques exist that help in solving large scale problems where complex series appear. Most of these acceleration techniques have been implemented in algorithms for planar problems;however it is also possible to implement some of these techniques in the series summations appearing in the analysis of conformal antennas. Two adequate summation acceleration techniques are investigated and modified in order to achieve better convergence. The obtained results show excellent convergence properties and applying this method to conformal antenna problem shows that significant reductions in computational time and memory usage can be achieved.
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