When using CUDA, developers program in popular languages such as C, C , Fortran, Python and MATLAB and express parallelism through extensions in the form of a few basic keywords.ĬUDA accelerates applications across a wide range of domains from image processing, to deep learning, numerical analytics and computational science. In GPU-accelerated applications, the sequential part of the workload runs on the CPU – which is optimized for single-threaded performance – while the compute intensive portion of the application runs on thousands of GPU cores in parallel. With CUDA, developers are able to dramatically speed up computing applications by harnessing the power of GPUs. IntroductionĬUDA® is a parallel computing platform and programming model developed by NVIDIA for general computing on graphical processing units (GPUs). It also shows how both the block and thread dimensions can be three dimensional.This repository contains notes and examples to get started Parallel Computing with CUDA. This will become more important as we look at optimization in the future.īelow is a sample code with the predefined variables for a look at their possible values for one thread. In the non-idea situation where not all threads perform the same operation warp divergence occurs, and operations are serialized. This is referred to as single instruction, multiple thread (SIMT). Each warp executes the same operation on multiple pieces of data, in the optimal situation. The threads on Nvidia’s GPU are organized into warps consisting of 32 threads. Out of the variables listed warp size hasn’t been mentioned yet. Predefined variables in CUDA Kernels dim3 gridDim Int blockCount = ceil(problemSize/threadCount) may result in launching more threads than needed though Simple way to ensure enough threads are launched On average a good thread count, the best thread count varies based on the situation Conditional statement to exit if index (globalThreadId) is out of bounds A potential way to way to parallelize a CPU loop on the GPU. The next is a skeleton code that shows one way to launch a large number of threads for a problemSize that could vary, by using ceiland a conditional statement in the kernel. It is a good practice to include this and it will help ensure profiling is successful (a future post). For example, a loop on the host with no dependencies between iterations can easily be converted to run on the GPU using this style of indexing.Īt the end of the above sample cudaDeviceReset() was added. This style of computing an index is used for many kernels. One of the things to point out is the use of the block and thread indices to compute a globalThreadId. My threadIdx.x is 1, blockIdx.x is 1, blockDim.x is 2, Global thread id is 3 My threadIdx.x is 0, blockIdx.x is 1, blockDim.x is 2, Global thread id is 2 My threadIdx.x is 1, blockIdx.x is 0, blockDim.x is 2, Global thread id is 1 My threadIdx.x is 0, blockIdx.x is 0, blockDim.x is 2, Global thread id is 0 to ensure that the device work has completed Destroys and cleans up all resources associated with the current device. ![]() This call waits for all of the submitted GPU work to complete Call a device function from the host: a kernel launch The code will default to 0 if not called though ThreadIdx.x, blockIdx.x, blockDim.x, globalThreadId) Printf("My threadIdx.x is %d, blockIdx.x is %d, blockDim.x is %d, Global thread id is %d\n", Int globalThreadId = blockIdx.x * blockDim.x threadIdx.x globalThreadId: The id of the thread with respect to the whole kernel blockDim.x: The number of threads in a block (block's dimension) From 0 - (number of blocks launched - 1) ![]() blockIdx.x: The block id with respect to the grid (all blocks in the kernel) threadIdx.x: The thread id with respect to the thread's block
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