HowTos

1. Compile pytorch CUDA extension with multiple Compute Capabilities as well as PTX. https://pytorch.org/docs/stable/cpp_extension.html env TORCH_CUDA_ARCH_LIST="6.1 7.5 8.6+PTX" python setup.py install

Quering device properties.

cudaDeviceGetAttribute argued being faster , though it's too verbose

  int maxBlockDimX;
  int maxBlockDimY;
  int maxBlockDimZ;
  cudaDeviceGetAttribute(&maxBlockDimX, cudaDevAttrMaxBlockDimX, devIdx);
  cudaDeviceGetAttribute(&maxBlockDimY, cudaDevAttrMaxBlockDimY, devIdx);
  cudaDeviceGetAttribute(&maxBlockDimZ, cudaDevAttrMaxBlockDimZ, devIdx);
  printf("  %d %d %d \n", maxBlockDimX, maxBlockDimY, maxBlockDimZ);

vs. cudaGetDeviceProperties

  cudaDeviceProp prop;
  cudaGetDeviceProperties(&prop, devIdx);
  printf("  Max Threads Dim: %d %d %d\n", prop.maxThreadsDim[0], prop.maxThreadsDim[1], prop.maxThreadsDim[2]);

• reference of CUDA device properties

• Hemi - library for writting reusable CPU and GPU code. single kernel function executable on both device types. More in this blog post.

1. Compile CUDA kernels

nvcc device_info.cu -o device_info

2. Theory Thread Hierarchy with easy examples.

1d(Dx=15)
like finiding a point on a line
threadIdx = (7)
threadId = threadIdx.x
threadId = 7

-> -------x-------

2d(Dx=15, Dy=4)
like finding a line on a plane and then finding a point on it.
threadIdx = (2, 7)
threadId = threadIdx.x + threadIdx.y * Dx
threadId = 7 + 2 * 15 = 37
   ---------------
   ---------------
-> -------x-------
   ---------------



3d(Dx=15, Dy=4, Dz=2)
threadIdx = (0, 2, 7)
threadId = threadIdx.x + threadIdx.y * Dx + threadIdx.z * Dx * Dy
like finding a plane in a volume then line on a plane and finaly a point.

   --------------- --
   ---------------   | first
-> --------x------   | find a slice
   --------------- --

   ---------------
   --------------- 
   --------------- 
   ---------------