![]() Note: on Windows, you need to make sure you set Platform to 圆4 in the Configuration Properties for your project in Microsoft Visual Studio. Moreover, there is a race condition since multiple parallel threads would both read and write the same locations. This is only a first step, because as written, this kernel is only correct for a single thread, since every thread that runs it will perform the add on the whole array. Check for errors (all values should be 3.0f)įor (int i = 0 i nvcc add.cu -o add_cuda Wait for GPU to finish before accessing on host Kernel function to add the elements of two arrays To do this I just call cudaDeviceSynchronize() before doing the final error checking on the CPU. ![]() Just one more thing: I need the CPU to wait until the kernel is done before it accesses the results (because CUDA kernel launches don’t block the calling CPU thread). add>(N, x, y) Įasy! I’ll get into the details of what goes inside the angle brackets soon for now all you need to know is that this line launches one GPU thread to run add(). I just have to add it to the call to add before the parameter list. CUDA Kernel function to add the elements of two arrays on the GPUįor (int i = 0 i >. To do this, all I have to do is add the specifier _global_ to the function, which tells the CUDA C compiler that this is a function that runs on the GPU and can be called from CPU code. It’s actually pretty easy to take the first steps.įirst, I just have to turn our add function into a function that the GPU can run, called a kernel in CUDA. Now I want to get this computation running (in parallel) on the many cores of a GPU. \add.)Īs expected, it prints that there was no error in the summation and then exits. (On Windows you may want to name the executable add.exe and run it with. ![]() function to add the elements of two arraysįor (int i = 0 i clang add.cpp -o add We’ll start with a simple C program that adds the elements of two arrays with a million elements each. You can also follow along with a Jupyter Notebook running on a GPU in the cloud. You’ll also need the free CUDA Toolkit installed. To follow along, you’ll need a computer with an CUDA-capable GPU (Windows, Mac, or Linux, and any NVIDIA GPU should do), or a cloud instance with GPUs (AWS, Azure, IBM SoftLayer, and other cloud service providers have them). If you are a C or C programmer, this blog post should give you a good start. So, you’ve heard about CUDA and you are interested in learning how to use it in your own applications. Many developers have accelerated their computation- and bandwidth-hungry applications this way, including the libraries and frameworks that underpin the ongoing revolution in artificial intelligence known as Deep Learning. It lets you use the powerful C programming language to develop high performance algorithms accelerated by thousands of parallel threads running on GPUs. ![]() But CUDA programming has gotten easier, and GPUs have gotten much faster, so it’s time for an updated (and even easier) introduction.ĬUDA C is just one of the ways you can create massively parallel applications with CUDA. I wrote a previous post, Easy Introduction to CUDA in 2013 that has been popular over the years. This post is a super simple introduction to CUDA, the popular parallel computing platform and programming model from NVIDIA.
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