What is CUDA dynamic parallelism?

CUDA dynamic parallelism is an extension to the CUDA programming model enabling a CUDA kernel to create new thread grids by launching new kernels. Dynamic parallelism is introduced with the Kepler architecture, first appearing in the GK110 chip. In previous CUDA systems, kernels can only be launched from the host code.

What is dynamic parallelism?

Under dynamic parallelism, one kernel may launch another kernel, and that kernel may launch another, and so on. Each subordinate launch is considered a new “nesting level,” and the total number of levels is the “nesting depth” of the program.

How many threads is a warp?

32 threads
A warp is a set of 32 threads within a thread block such that all the threads in a warp execute the same instruction. These threads are selected serially by the SM. Once a thread block is launched on a multiprocessor (SM), all of its warps are resident until their execution finishes.

How does parallelism work in CUDA?

Parallelism on GPUs is SIMT (Single Instruction Multiple Threads). For CUDA Kernels, you specify a grid of blocks where every block has N threads. The CUDA library does all the trick and the CUDA Compiler (nvcc) generates the GPU code which is executed by the GPU.

How does parallelism work in OpenCL?

Predominantly data parallelism, but there’s also some task parallelism involved. In your image processing example a kernel might do the processing for a single output pixel. You’d instruct OpenCL or CUDA to run as many threads as there are pixels in the output image. It then schedules those threads to run on the GPU/CPU that you’re targeting.

What is the difference between OpenCL and CUDA?

OpenCL follows the same paradigm but you compile yor kernel (if they are not precompiled) at runtime. Specify the number of threads to execute the kernel and the lib does the rest. The best way to learn CUDA (OpenCL) is to look in the CUDA Programming Guide ( OpenCL Programming Guide) and look at the samples in the GPU Computing SDK.

How does the CUDA library work?

The CUDA library does all the trick and the CUDA Compiler (nvcc) generates the GPU code which is executed by the GPU. The CUDA library tells the GPU driver and further more the thread scheduler on the GPU how many threads should execute the kernel ( (number of blocks) x (number of threads)).