cuda notes
cuda c extends c by allowing the programmer to define c functions, called kernels, that, when called are excecuted n times in parallel by n different cuda kernels, as opposed to only once like regular c functions.
a kernel is defined using global declaration specifier and the number of cuda threads that execute that kernel for a given kernel call is specified using a new <<<...>>> excecution configuration syntax. each thread that ececutes the kernel is given a unique thread id that is accessible within the kernel through the built in threadIdx variable
the following code add two vectors a and b of size n and stores the result into a
vector c
here each of the N threads that excecute VecAdd() performs one pair wise addition
For convenience threadIdx is a 3 component vector, so that threads can be identified using a one dimensional, two dimensional or three dimensional thread index, forming a one dimensional, two dimensional or three dimensional blick of threads called thread blick this provides a natural wat to invoke computation across the elements in a domain such as a vector, matrix, or volume.
the index of a thread and its thread id relate to each other in astraight forward way . For a one dimensional block, they are the same ; for a two dimensional block of size Dx, Dy the thread id of a thread of index x,y is x+yDx; for a three dimensional block og size Dx, Dy , Dz the thread id of a thread of index x,y,z is x+yDx+zDxDy
for example the code below adds two matricesa A and B of size NXN and stores the result into matrix c
there is a limit to the number of threads per block, since all threads of a block are expected to reside on the same processor core and must share the limited memory resources of that core. on current gpus a thread block may contain up to 1024 threads.
however, a kernel can be executed by multiple equally shaped thread blocks, so thaat the total number of threads is equal to the number of threads per block times the number of blocks.
blocks are organized into a one dimensional, two dimensional or three dimensional grid of thread blocks
each block within the grid can be identified by a one dimensional , two dimensioanl or three dimensional index accessible within the kernek through the built-in blockIdx variable. the dimesnion of the thred block is accessible within the kernel through the builtin blockDim variable.
Given the heterogeneous nature of the cuda programming model, a typical sequence of operations for a cuda c program is
- Declare and allocate host and device memory.
- initialize host data.
- transfer data from the host to device
- excecute one or more kernels.
- transfer results from the device to the host.
saxpy single precision a*x plus y it is a good hello world example for parallel computaion