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01-Applied Mathematics & Information Sciences
An International Journal
               
 
 
 
 
 
 
 
 
 
 
 

Content
 

Volumes > Volume 5 > No. 5-2S

 
   

Data Layout Pruning on GPU

PP: 129S-138S
Author(s)
Xinbiao Gan, Zhiying Wang, Li Shen and Qi Zhu,
Abstract
This work is based on NVIDIA GTX 280 using CUDA (Computing Unified Device Architecture). We classify Dataset to be transferred into CUDA memory hierarchy into SW (shared and must write) and SR (shared but only read), and existing memory spaces (including shared memory, constant memory, texture memory and global memory) supported on CUDA-enabled GPU memory hierarchy are adopted to probe into best memory space for specified dataset. Conclusions from experimental results are that shared memory is proposed for SW; constant memory is advisable for SR and texture memory for SR with structured-grid dataset, especially for 2D, 3D regular grid.

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