![]() =3355= NVPROF is profiling process 3355, command. I think the simplest way to find out how long the kernel takes to run is to run it with nvprof, the command line GPU profiler that comes with the CUDA Toolkit. 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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