CUDA_HOME path for Tensorflow
Run the following command in the terminal: export CUDA_HOME=/usr/local/cuda-X.X Where you replace X.X by the first two digits of your version number (can be found out e.g. via nvcc –version).
Run the following command in the terminal: export CUDA_HOME=/usr/local/cuda-X.X Where you replace X.X by the first two digits of your version number (can be found out e.g. via nvcc –version).
You need to ensure that your driver version matches or exceeds your CUDA Toolkit version. For 2.3 you need a 190.x driver, for 3.0 you need 195.x and for 3.1 you need 256.x (actually anything up to the next multiple of five is ok, e.g. 258.x for 3.1). You can check your driver version by …
In very broad terms: cuda.h defines the public host functions and types for the CUDA driver API. cuda_runtime_api.h defines the public host functions and types for the CUDA runtime API cuda_runtime.h defines everything cuda_runtime_api.h does, as well as built-in type definitions and function overlays for the CUDA language extensions and device intrinsic functions. If you …
Set the environment variable CUDA_DEVICE_ORDER as: export CUDA_DEVICE_ORDER=PCI_BUS_ID Then the GPU IDs will be ordered by pci bus IDs.
I think it depends on your application (otherwise, why would they provide both ways?) Mapped, pinned memory (zero-copy) is useful when either: The GPU has no memory on its own and uses RAM anyway You load the data exactly once, but you have a lot of computation to perform on it and you want to …
The answer to the short question is “No”. Warp level branch divergence around a __syncthreads() instruction will cause a deadlock and result in a kernel hang. Your code example is not guaranteed to be safe or correct. The correct way to implement the code would be like this: __global__ void kernel(…) if (tidx < N) …
Define the class in a header that you #include, just like in C++. Any method that must be called from device code should be defined with both __device__ and __host__ declspecs, including the constructor and destructor if you plan to use new/delete on the device (note new/delete require CUDA 4.0 and a compute capability 2.0 …
Roughly speaking, the code compilation flow goes like this: CUDA C/C++ device code source –> PTX –> SASS The virtual architecture (e.g. compute_20, whatever is specified by -arch compute…) determines what type of PTX code will be generated. The additional switches (e.g. -code sm_21) determine what type of SASS code will be generated. SASS is …
UPDATE GPU Version __global__ void hash (float *largeFloatingPointArray,int largeFloatingPointArraySize, int *dictionary, int size, int num_blocks) { int x = (threadIdx.x + blockIdx.x * blockDim.x); // Each thread of each block will float y; // compute one (or more) floats int noOfOccurrences = 0; int a; while( x < size ) // While there is work …