nvidia

What is the relationship between a CUDA core, a streaming multiprocessor and the CUDA model of blocks and threads? What gets mapped to what and what is parallelized and how? and what is more efficient, maximize the number of blocks or the number of threads? My current understanding is that there are 8 cuda cores per multiprocessor. and that every cuda core will be able to execute one cuda block at a time. and all the threads in that block are executed serially in that particular core. Is this correct? Edric The thread / block layout is described in detail in the CUDA programming guide . In

I'm programming a DirectX game, and when I run it on an Optimus laptop the Intel GPU is used, resulting in horrible performance. If I force the NVIDIA GPU using the context menu or by renaming my executable to bf3.exe or some other famous game executable name, performance is as expected. Obviously neither is an acceptable solution for when I have to redistribute my game, so is there a way to programmatically force the laptop to use the NVIDIA GPU? I've already tried using DirectX to enumerate adapters (IDirect3D9::GetAdapterCount, IDirect3D9::GetAdapterIdentifier) and it doesn't work: only 1

I'm new to the CUDA paradigm. My question is in determining the number of threads per block, and blocks per grid. Does a bit of art and trial play into this? What I've found is that many examples have seemingly arbitrary number chosen for these things. I'm considering a problem where I would be able to pass matrices - of any size - to a method for multiplication. So that, each element of C (as in C = A * B) would be calculated by a single thread. How would you determine the threads/block, blocks/grid in this case? In general you want to size your blocks/grid to match your data and

My monte carlo pi calculation CUDA program is causing my nvidia driver to crash when I exceed around 500 trials and 256 full blocks. It seems to be happening in the monteCarlo kernel function.Any help is appreciated. #include <stdio.h> #include <stdlib.h> #include <cuda.h> #include <curand.h> #include <curand_kernel.h> #define NUM_THREAD 256 #define NUM_BLOCK 256 /////////////////////////////////////////////////////////////////////////////////////////// /////////////////////////////////////////////////////////////////////////////////////////// // Function to sum an array __global__ void

I was testing the new CUDA 8 along with the Pascal Titan X GPU and is expecting speed up for my code but for some reason it ends up being slower. I am on Ubuntu 16.04. Here is the minimum code that can reproduce the result: CUDASample.cuh class CUDASample{ public: void AddOneToVector(std::vector<int> &in); }; CUDASample.cu __global__ static void CUDAKernelAddOneToVector(int *data) { const int x = blockIdx.x * blockDim.x + threadIdx.x; const int y = blockIdx.y * blockDim.y + threadIdx.y; const int mx = gridDim.x * blockDim.x; data[y * mx + x] = data[y * mx + x] + 1.0f; } void CUDASample:

When a computer has multiple CUDA-capable GPUs, each GPU is assigned a device ID . By default, CUDA kernels execute on device ID 0 . You can use cudaSetDevice(int device) to select a different device. Let's say I have two GPUs in my machine: a GTX 480 and a GTX 670. How does CUDA decide which GPU is device ID 0 and which GPU is device ID 1 ? Ideas for how CUDA might assign device IDs (just brainstorming): descending order of compute capability PCI slot number date/time when the device was added to system (device that was just added to computer is higher ID number) Motivation : I'm working on