RocSPARSE notion is that the user is responsible for such buffer allocation and deallocation. Many routines exposed by the rocSPARSE API require a temporary storage buffer on the device. In public API header files, rocSPARSE only relies on functions, pointers, forward declared structs, enumerations and type defs.Īdditionally, rocSPARSE introduces multiple library and object handles by using opaque types to hide layout and implementation details from the user. This is especially helpful to offer a thin C89 API to the user and still get all the convenience of C++.Īs a side effect, ABI related binary compatibility issues can be avoided.įurthermore, this approach allows rocSPARSE routines to be used by other programming languages. Blocks will load to fill up the SMs, we will have 16 blocks finish at roughly the same time, and as the first 4 SMs free up, they will start processing the last 4 blocks (NOT necessarily blocks #17-20).The rocSPARSE library is developed using the Hourglass API approach. Every time a block is run, a SM will have only 31 of its 32 cores busy. If we have a simple scenario where we have 16 SMs with 32 CUDA cores each, and we have 31x1x1 block size, and 20x1x1 grid size, we will forfeit at least 1/32 of the processing power of the card. So you cannot have blocks with more threads than CUDA cores are contained in a SM. Threads in a block HAVE TO be on the same SM, to use its facilities of shared memory and synchronization. As far as I know, the dimensionality of a block or grid is just a logical assignment irrelevant of hardware, but the total size of a block (x*y*z) is very important. GeForce 690 has 2) -> multiple SM's (streaming multiprocessors) -> multiple CUDA cores. You can inspect the generated files by adding -keep to your nvcc command line.ĬUDA CDP works similar to the CUDA Runtime API described above.īasically, the GPU is divided into separate "device" GPUs (e.g. _host_ _device_ dim3(unsigned int vx = 1, unsigned int vy = 1, unsigned int vz = 1) : x(vx), y(vy), z(vz) ![]() * the declaration of dim3 from vector_types.h of CUDA/include */ gridDim.x is the upper bound of blockIdx.x, this is not that obvious for people like me. So, for me, gridDim & blockDim is like some boundaries.Į.g. So I'd like to keepĪrr_on_device = arr_on_device * arr_on_device I thoughtįorce user to use *kernel>* would be better. this just brroke the semantics of both C and C++. It's not C style, and C++ style ? at first, I thought this could be done byĬ++'s constructor stuff, but I checked structure *dim3*, there's no properĬonstructor for this. Kernel>() this is exactly the same thing with above. Kernel>() means kernel will execute in 10 blocks each have 32 threads. Int idx = blockIdx.x * blockDIm.x + threadIdx.x if I was the CUDA authore, I should make the kernel function more ![]() so, kernel function is so different from the *normal*Ĭ/C++ functions. If there's any parameter passed into _global_ function, it should be stored and a _global_ function could only return void. Note, _global_ means this function will be called from host codes,Īnd executed on device. ![]() Normally, we write kernel function like this. Here I tried to self-explain the CUDA launch parameters model (or execution configuration model) using some pseudo codes, but I don't know if there were some big mistakes, So hope someone help to review it, and give me some advice.
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