The PDB file is a Visual Studio specific file that has the debugging symbols for your project. You can ignore those messages, unless you’re hoping to step into the code for those dlls with the debugger (which is doubtful, as those are system dlls). In other words, you can and should ignore them, as you … Read more
The resource which is being exhausted is time. On all current CUDA platforms, the display driver includes a watchdog timer which will kill any kernel which takes more than a few seconds to execute. Running code on a card which is running a display is subject to this limit. On the WDDM Windows platforms you … Read more
CUDA has 2 primary APIs, the runtime and the driver API. Both have a corresponding version (e.g. 8.0, 9.0, etc.) The necessary support for the driver API (e.g. libcuda.so on linux) is installed by the GPU driver installer. The necessary support for the runtime API (e.g. libcudart.so on linux, and also nvcc) is installed by … Read more
When I compile and run your code, I get: an illegal memory access was encountered-3 printed out. You may indeed be getting “unspecified launch failure” instead. The exact error reporting will depend on CUDA version, GPU, and platform. But we can proceed forward regardless. Either message indicates that the kernel launched but encountered an error, … Read more
TL;DR) See this table: https://www.tensorflow.org/install/source#gpu Generally: Check the CUDA version: cat /usr/local/cuda/version.txt and cuDNN version: grep CUDNN_MAJOR -A 2 /usr/local/cuda/include/cudnn.h and install a combination as given below in the images or here. The following images and the link provide an overview of the officially supported/tested combinations of CUDA and TensorFlow on Linux, macOS and Windows: … Read more
There are two parts to that answer (I wrote it). One part is easy to quantify, the other is more empirical. Hardware Constraints: This is the easy to quantify part. Appendix F of the current CUDA programming guide lists a number of hard limits which limit how many threads per block a kernel launch can … Read more
Probably the best way to check for errors in runtime API code is to define an assert style handler function and wrapper macro like this: #define gpuErrchk(ans) { gpuAssert((ans), __FILE__, __LINE__); } inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort=true) { if (code != cudaSuccess) { fprintf(stderr,”GPUassert: %s %s %d\n”, cudaGetErrorString(code), file, … Read more
It’s optimization on level 3, basically a shortcut for several other options related to speed optimization etc. (see link below). I can’t find any documentation on it. … it is one of the best known options: https://gcc.gnu.org/onlinedocs/gcc/Optimize-Options.html http://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/#options-for-altering-compiler-linker-behavior