The CUDA threads hierarchy can be 3-dimensional. ThreadIdx.x is index of the thread within a block (starting at 0).Īs you noted, we have been using the suffix. All blocks in a grid contain the same number of threads. ![]() GridDim.x is the number of the blocks in the grids.īlockIdx.x is the index of the current block within the grid.īlockDim.x is the number of threads in the block. These variables describe the thread, thread block, and grid. In the kernel’s code, we can access variables provided by CUDA. Each gray rectangle represents a block.The green rectangle represents the grid. In the drawing, each blue rectangle represents a thread. printHelloGPU>() is configured to run in 5 thread blocks which each have five threads and will, therefore, run 25 times.printHelloGPU>() is configured to run in 5 thread blocks which each have a single thread and will, therefore, run five times.printHelloGPU>() is configured to run in a single thread block which has 5 threads and will, therefore, run 5 times.printHelloGPU>() is configured to run in a single thread block which has a single thread and will, therefore, run only once.Thus, under the assumption that a kernel called printHelloGPU has been defined, the following are true: The syntax for this is:Ī kernel is executed once for every thread in every thread block configured when the kernel is launched. The execution configuration allows programmers to specify details about launching the kernel to run in parallel on multiple GPU threads. Notice, in the previous example, the kernel is launching with 1 block of threads (the first execution configuration argument) which contains 1 thread (the second configuration argument). How many threads to execute in each block. When launching a kernel, we must provide an execution configuration, which is done by using the > syntax.Īt a high level, the execution configuration allows programmers to specify the thread hierarchy for a kernel launch, which defines the number of thread blocks, as well as.When calling a function to run on the GPU, we call this function a kernel (In the example, printHelloGPU is the kernel).It is required that functions defined with the _global_ keyword return type void.The code executed on the CPU is referred to as host code, and code executed on the GPU is referred to as device code.The _global_ keyword indicates that the following function will run on the GPU.Let’s remember some concepts we learned in a previous post: The cudaDeviceSyncronize function determines that all the processing on the GPU must be done before continuing. Now, let’s change this code to run on the GPU. To get started, let’s write something straightforward to run on the CPU. ![]() If you are starting with CUDA and want to know how to setup your environment, using VS2017, I recommend you to read this post. We will not cover all aspects, but it could be a nice first step. In this post, I would like to explain a basic but confusing concept of CUDA programming: Thread Hierarchies.
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