Introduction to cuda pdf

While generally subefficient on large sequences compared to algorithms with better asymptotic algorithmic complexity i. Refer to the excellent tutorial by H. Requires Compute Capability 2. A simple demonstration of global memory atomic instructions. Performance improvement due to use of L2 access policy window can only be noticed on Compute capability 8. This sample is a simple code that illustrates basic usage of cooperative groups within the thread block.

Simple example that demonstrates how to use a new CUDA 4. Devices without HyperQ SM 2. Requires Compute Capability 3. This sample illustrates the usage of CUDA streams to achieve overlapping of kernel execution with data copies to and from the device. This application demonstrates how to use the new CUDA 4. This sample demonstrates the basic usage of the CUDA occupancy calculator and occupancy-based launch configurator APIs by launching a kernel with the launch configurator, and measures the utilization difference against a manually configured launch.

This sample demonstrates a CUDA 5. This example demonstrates how to pass in a GPU device function from the GPU device static library as a function pointer to be called.

This sample requires devices with compute capability 2. This sample uses a new CUDA 4. This sample is a templatized version of the template project. It also shows how to correctly templatize dynamically allocated shared memory arrays. This sample uses the new CUDA 4. This sample illustrates how to use Zero MemCopy, kernels can read and write directly to pinned system memory. A trivial template project that can be used as a starting point to create new CUDA projects.

It is the same as the sample illustrating Chapter 3 of the programming guide with some additions like error checking. Vector addition kernel demonstrated is the same as the sample illustrating Chapter 3 of the programming guide. We choose to use the Open Source package Numba. Therefore we have to import both numpy as well as the cuda module:.

Below is our first CUDA kernel! You can see that we simply launched the previous kernel using the command cudakernel0[1, 1] array. But what is the meaning of [1, 1] after the kernel name? On the GPU, the computations are executed in separate blocks, and each of these blocks uses many threads. The set of all the blocks is called a grid. Therefore the grid size is equal to the number of blocks used during a computation. Furthermore, each block uses the same number of threads.

We call this number the block size. Therefore in the previous example, [1, 1] indicates that we are using only one thread to launch the kernel. The result when using so many threads is quite weird. The above kernel is launched with a big number of threads.