Recent GPU versions of tensorflow require compute capability 3.5 or higher (and use cuDNN to access the GPU. cuDNN also requires a GPU of cc3.0 or higher: cuDNN is supported on Windows, Linux and MacOS systems with Pascal, Kepler, Maxwell, Tegra K1 or Tegra X1 GPUs. Kepler = cc3.x Maxwell = cc5.x Pascal = cc6.x … Read more
something like this should work (coded in browser, not tested): // N is the maximum number of structs to insert #define N 10000 typedef struct { int A, B, C; } Match; __device__ Match dev_data[N]; __device__ int dev_count = 0; __device__ int my_push_back(Match * mt) { int insert_pt = atomicAdd(&dev_count, 1); if (insert_pt < N){ … Read more
For best performance, one would want to map the 128-bit type on top of a suitable CUDA vector type, such as uint4, and implement the functionality using PTX inline assembly. The addition would look something like this: typedef uint4 my_uint128_t; __device__ my_uint128_t add_uint128 (my_uint128_t addend, my_uint128_t augend) { my_uint128_t res; asm (“add.cc.u32 %0, %4, %8;\n\t” … Read more
The problem was caused by not setting the CUDA_VISIBLE_DEVICES variable within the shell correctly. To specify CUDA device 1 for example, you would set the CUDA_VISIBLE_DEVICES using export CUDA_VISIBLE_DEVICES=1 or CUDA_VISIBLE_DEVICES=1 ./cuda_executable The former sets the variable for the life of the current shell, the latter only for the lifespan of that particular executable invocation. … Read more
The necessary instructions are contained in the documentation for the MPS service. You’ll note that those instructions don’t really depend on or call out MPI, so there really isn’t anything MPI-specific about them. Here’s a walkthrough/example. Read section 2.3 of the above-linked documentation for various requirements and restrictions. I recommend using CUDA 7, 7.5, or … Read more
It is a sampled measurement over a time period. For a given time period, it reports what percentage of time one or more GPU kernel(s) was active (i.e. running). It doesn’t tell you anything about how many SMs were used, or how “busy” the code was, or what it was doing exactly, or in what … Read more
For nvidia (and amd for that matter) gpus the local memory is divided into memorybanks. Each bank can only address one dataset at a time, so if a halfwarp tries to load/store data from/to the same bank the access has to be serialized (this is a bank conflict). For gt200 gpus there are 16 banks … Read more
Update your NVIDIA driver. At the moment you have the driver which only supports CUDA 6 or lower, and you are trying to use the CUDA 7.0 toolkit with it.
You just need to make a custom class based off of DataGridView so you can enable its DoubleBuffering. That’s it! class CustomDataGridView: DataGridView { public CustomDataGridView() { DoubleBuffered = true; } } As long as all of my instances of the grid are using this custom version, all is well. If I ever run into … Read more
Hardware If a GPU device has, for example, 4 multiprocessing units, and they can run 768 threads each: then at a given moment no more than 4*768 threads will be really running in parallel (if you planned more threads, they will be waiting their turn). Software threads are organized in blocks. A block is executed … Read more