nvidia

I am very new to CUDA programming and was reading the 'CUDA C Programming Guide' provided by nvidia. ( http://developer.download.nvidia.com/compute/DevZone/docs/html/C/doc/CUDA_C_Programming_Guide.pdf ) In the page 25, it has the following C code that does the matrix multiplication. Can you please tell me how can I make that code run on two devices? (if I have two nvida CUDA capable cards installed in my computer). Can you please show me with an example. // Matrices are stored in row-major order: // M(row, col) = *(M.elements + row * M.stride + col) typedef struct { int width; int height; int

I'm having trouble installing driver for GTX980 on Ubuntu 14.04. I need to upgrade to CUDA7.5 and latest driver. I used both the .run installer and deb. installer and do the purging before the installation. Here is the log: Using built-in stream user interface -> Detected 8 CPUs online; setting concurrency level to 8. -> License accepted by command line option. -> Installing NVIDIA driver version 352.39. -> There appears to already be a driver installed on your system (version: 352.39). As part of installing this driver (version: 352.39), the existing driver will be uninstalled. Are you sure

Nvidia has offered an example about how to profile bandwidth between Host and Device, you can find codes here: https://developer.nvidia.com/opencl (search "bandwidth"). The experiment is carried on in an Ubuntu 12.04 64-bits computer. I am inspecting pinned memory and mapped accessing mode, which can be tested by invoke: ./bandwidthtest --memory=pinned --access=mapped The core test loop on Host-to-Device bandwidth is at around line 736~748. I also list them here and add some comments and context code: //create a buffer cmPinnedData in host cmPinnedData = clCreateBuffer(cxGPUContext, CL_MEM

NVIDIA Deep Learning Accelerator IP to be Integrated into Arm Project Trillium Platform, Easing Building of Deep Learning IoT Chips GPU Technology Conference — NVIDIA and Arm today announced that they are partnering to bring deep learning inferencing to the billions of mobile, consumer electronics and Internet of Things devices that will enter the global marketplace. Under this partnership, NVIDIA and Arm will integrate the open-source NVIDIA Deep Learning Accelerator (NVDLA) architecture into Arm’s Project Trillium platform for machine learning. The collaboration will make it simple for IoT

So I've been experimenting with the performance counter class in C# and have had great success probing the CPU counters and almost everything I can find in the windows performance monitor. HOWEVER, I cannot gain access to the "NVIDIA GPU" category... So for example, the following line of code is how it usually works. PerformanceCounter cpuCounter = new PerformanceCounter("Processor", "% Processor Time", "_Total"); That code works fine, but the GPU category that appeared in the performance monitor, just as the processor category did, is not accessible by C#. The following line of code attempts

I'm developing an OpenGL application for Windows XP. The target machine has 2 NVIDIA GeForce 9800GT video cards, which are needed because the application needs to have output 2 streams of analog video. The application itself has two OpenGL windows, one for each video card. Each video card is connected to one monitor. As for the code, it's based on a minimal OpenGL example . How can I know if the application is utilizing both video cards for rendering? At the moment, I don't care if the application only runs on Windows XP or only with NVIDIA video cards, I just need to know how the two are