Parallel Range Query processing on R-tree with Graphics Processing Units

Yu, Boseon; Kim, Hyun-Duk; Choi, Wonik; Kwon, Dongseop

doi:10.9717/kmms.2011.14.5.669

Cited by 3 publications

(1 citation statement)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sequential processing will works slower than parallel processing because it needs to finish an operation firstly before doing other operations [16]. Therefore time needed in CPU implementation is longer.…”

Section: Resultsmentioning

confidence: 99%

Time Complexity Measurement on CUDA-based GPU Parallel Architecture of Morphology Operation

Izmantoko¹,

Choi²

2013

Journal of Korea Multimedia Society

View full text Add to dashboard Cite

Operation time of a function or procedure is a thing that always needs to be optimized. Parallelizing the operation is the general method to reduce the operation time of the function. One of the most powerful parallelizing methods is using GPU. In image processing field, one of the most commonly used operations is morphology operation. Three types of morphology operations kernel, naïve, global and shared, are presented in this paper. All kernels are made using CUDA and work parallel on GPU. Four morphology operations (erosion, dilation, opening, and closing) using square structuring element are tested on MRI images with different size to measure the speedup of the GPU implementation over CPU implementation. The results show that the speedup of dilation is similar for all kernels. However, on erosion, opening, and closing, shared kernel works faster than other kernels.

show abstract

Section: Resultsmentioning

confidence: 99%

Time Complexity Measurement on CUDA-based GPU Parallel Architecture of Morphology Operation

Izmantoko¹,

Choi²

2013

Journal of Korea Multimedia Society

View full text Add to dashboard Cite

show abstract

A Parallel Processing Method for Partial Nodes in R*-tree Using GPU

Kim¹,

Oh²

2012

Journal of Korea Spatial Information Society

View full text Add to dashboard Cite

The R*-tree manages hierarchical nodes for efficient access of spatial data. We propose a method that maintains partial nodes of R*-tree in the GPU memory to improve efficiency using parallel processing. The proposed method attempts to load as many nodes as possible to the GPU memory. The new nodes are inserted to manage the rest of R*-tree nodes in the main memory. The experimental result shows that the proposed method is more efficient than the main memory based R*-tree.

show abstract