|Code Snippet Name||Code Snippet Description||Parent Node|
|Pixel Buffer Objects: Mixing CUDA and OpenGL within the same application||
Following is the source for the Doctor Dobb's Journal article Part 15 Using Pixel Buffer Objects with CUDA and OpenGL. This source includes Microsoft Visual Studio build files as well as a Linux command-line to build an executable.
Many thanks to Joe Stam at NVIDIA for providing the Visual Studio build files. Joe also notes you need to remove the following lines from perlinKernelPBO.cu:
|Using Vertex Buffer Objects with CUDA and very fast surface rendering with primitive restart||
Following is the source for the Doctor Dobb's Journal article for a future article entitled Using Vertex Buffer Objects with CUDA and OpenGL. This source includes Microsoft Visual Studio build files as well as a Linux command-line to build an executable.
This code demonstrates how to draw 3D points, wireframe and surfaces using the framework described in Part 15 of my Doctor Dobb's article Using Pixel Buffer Objects with CUDA and OpenGL. I left in some ifdef statements so you can verify for yourself the speed of using Primitive Restart to bypass PCI bus bandwidth limitations.
Many thanks to Joe Stam at NVIDIA for providing the Visual Studio build files. Joe also notes you need to remove the following lines from perlinKernelVBO.cu and change the uint variable in runCUDA to "unsigned int":
|Multiclass Support Vector Machine||
The scaling of serial algorithms cannot rely on the improvement of CPUs anymore. The performance of classical Support Vector Machine (SVM) implementations has reached its limit and the arrival of the multi core era requires these algorithms to adapt to a new parallel scenario. Graphics Processing Units (GPU) have arisen as high performance platforms to implement data parallel algorithms. In this project, it is described how a naïve implementation of a multiclass classifier based on SVMs can map its inherent degrees of parallelism to the GPU programming model and efficiently use its computational throughput. Empirical results show that the training and classification time of the algorithm can be reduced an order of magnitude compared to a classical solver, LIBSVM, while guaranteeing the same accuracy.
Please find attached the multisvm 2.0 release of the source code.
The link to the source code repository where future versions will be available is http://code.google.com/p/multisvm/
* Sample datasets were removed due to their large file size. These can be obtained from the code repository site or the LIBSVM site.
** To compile the code please add the following CUDA libraries to the bin folder of the project or download the release code from the google code repository (which already contains these as part of the visual studio solution).
|Nexus version of Perlin simple VBO example from Doctor Dobb's Journal Part 18||
For convenience, this code snippet provides a version of the Parallel Nsight code that is pretty much ready to build in Visual Studio.
Note that there are three #define statements in simpleVBO.cpp that define what OpenGL rendering call is to be used. These are: