nvidia

Is there any way to debug OpenCL kernels on an Nvidia GPU, i.e. set breakpoints and inspect variables? My understanding is that Nvidia's tool does not allow OpenCL debugging, and AMD's and Intel's only allow it on their own devices. gDEBugger might help you somewhat (never used it though), but other than that there isn't any tool that I know of that can set breakpoints or inspect variables inside a kernel. Perhaps try to save intermediate outputs from your kernel if it is a long kernel. Sorry I can't give you a magic solution, debugging OpenCL is just hard. 来源： https://stackoverflow.com

I was certain that if you bind a buffer via glBindBuffer() , you can safely assume that it stays bound, until the target is rebound through another call to glBindBuffer() . I was therefore quite surprised when I discovered that calling glBindVertexArray() sets the buffer bound to the GL_ELEMENT_ARRAY target to 0. Here's the minimal C++ sample code: GLuint buff; glGenBuffers(1, &buff); std::cout << "Buffer is " << buff << "\n"; glBindBuffer(GL_ELEMENT_ARRAY_BUFFER, buff); GLuint vao; glGenVertexArrays(1, &vao); GLint bound_buff; glGetIntegerv(GL_ELEMENT_ARRAY_BUFFER_BINDING, &bound_buff); std:

Is is possible to run a Java3D application on Nvidia 3D Vision hardware? I've got an existing Java3D application that can run in stereoscopic 3D. In the past, I've always run the application on Quadro cards using the OpenGL renderer and quad buffered stereo. I now have access to a laptop with the nVidia 3D Vision system (with a GeForce GTX 460M). From the documentation, it seems like it should be possible to run my application in stereo if I use the DirectX bindings and let the nVidia drivers take care of the stereo, however, this does not seem to be the case. If I run a Java3D application

I need to compute the median of an array of size p inside a CUDA kernel (in my case, p is small e.g. p = 10). I am using an O(p^2) algorithm for its simplicity, but at the cost of time performance. Is there a "function" to find the median efficiently that I can call inside a CUDA kernel? I know I could implement a selection algorithm, but I'm looking for a function and/or tested code. Thanks! Domi Here are a few hints: Use a better selection algorithm: QuickSelect is a faster version of QuickSort for selecting the kth element in an array. For compile-time-constant mask sizes, sorting networks

I'm using tensorflow with Titan-X GPUs and I've noticed that, when I run the CIFAR10 example, the Volatile GPU-utilization is pretty constant around 30%, whereas when I train my own model, the Volatile GPU-utilization is far from steady, it is almost always 0% and spikes at 80/90% before going back to 0%, over and over again. I thought that this behavior was due to the way I was feeding the data to the network (I was fetching the data after each step, which took some time). But after implementing a queue to feed the data and avoid this latency between steps, the problem persisted (see below

What is the maximum number of blocks in a grid that can created per kernel launch? I am slightly confused here since Now the compute capability table here says that there can be 65535 blocks per grid dimemsion in CUDA compute capability 2.0. Does that mean the total number of blocks = 65535*65535? Or does it mean that you can rearrange at most 65535 into a 1d grid of 65536 blocks or 2d grid of sqrt(65535) * sqrt(65535) ? Thank you. 65535 per dimension of the grid. On compute 1.x cards, 1D and 2D grids are supported. On compute 2.x cards, 3D grids are also supported, so 65535, 65535 x 65535,