scholarly journals Computing OpenSURF on OpenCL and General Purpose GPU

10.5772/57057 ◽  
2013 ◽  
Vol 10 (10) ◽  
pp. 375 ◽  
Author(s):  
Wanglong Yan ◽  
Xiaohua Shi ◽  
Xin Yan ◽  
Lina Wang
Keyword(s):  
General Purpose ◽  
General Purpose Gpu

Real-Time Bilateral Filtering Using GPGPU

2015 ◽  
Vol 781 ◽  
pp. 568-571 ◽  
Author(s):  
Sanun Srisuk ◽  
Wachirapong Kesjindatanawaj ◽  
Surachai Ongkittikul
Keyword(s):  
Nonlinear Filtering ◽  
Computation Time ◽  
General Purpose ◽  
Experimental Results ◽  
Bilateral Filtering ◽  
Image Smoothing ◽  
Edge Preserving ◽  
Filter Kernel ◽  
Sharp Edges ◽  
General Purpose Gpu

In this paper, we present a technique for accelerating the bilateral filtering using GPGPU. Bilateral filtering is a tool for an image smoothing with edge preserving properties. It serves as a mixture of domain and range filters. Domain filter suppresses Gaussian noise while range filter maintains sharp edges. Bilateral filtering is a nonlinear filtering in which the filter kernel must be computed pixel by pixel. Therefore conventional fast Fourier transform technique cannot be used to accelerate the bilateral filtering. Instead, general purpose GPU is used as a parallel machine to reduce time consuming of the bilateral filtering. We will show the experimental results by comparing the computation time of CPU and GPU. It was cleared that, from the experimental results, GPU outperformed the CPU in terms of computation time.

Computing Gamma Calculus on Computer Cluster

2012 ◽  
pp. 2016-2026
Author(s):  
Hong Lin ◽  
Jeremy Kemp ◽  
Padraic Gilbert
Keyword(s):  
Gpu Computing ◽  
Programming Model ◽  
Large Data ◽  
General Purpose ◽  
Tuple Space ◽  
Data Set ◽  
Cuda Architecture ◽  
High Level ◽  
General Purpose Gpu ◽  
Grid Cluster

Gamma Calculus is an inherently parallel, high-level programming model, which allows simple programming molecules to interact, creating a complex system with minimum of coding. Gamma calculus modeled programs were written on top of IBM’s TSpaces middleware, which is Java-based and uses a “Tuple Space” based model for communication, similar to that in Gamma. A parser was written in C++ to translate the Gamma syntax. This was implemented on UHD’s grid cluster (grid.uhd.edu), and in an effort to increase performance and scalability, existing Gamma programs are being transferred to Nvidia’s CUDA architecture. General Purpose GPU computing is well suited to run Gamma programs, as GPU’s excel at running the same operation on a large data set, potentially offering a large speedup.

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