CUDA threads may access data from multiple memory spaces during their execution. Kernels can only operate out of device memory, so the runtime provides functions to allocate, deallocate, and copy device memory, as well as transfer data between host memory and device memory. A host thread can set the device it operates on at any time by calling cudaSetDevice().Īs discussed earlier, the CUDA programming model assumes a system composed of a host and a device, each with their own separate memory. , this destroys the primary context of the device the host thread currently operates on. When a host thread calls cudaDeviceReset() The runtime does not expose the primary context to the application. This context is the primary context for this device and it is shared among all the host threads of the application. Has retuned an error, prints the associated error message, and exists the application with ERROR FAILURE code.ĬUDA runtime function is initialized when it is called first time and during initialization, the runtime creates a CUDA context for each device in the system. In all the programs, CUDA_SAFE_CALL() that surrounds CUDA API calls is a utility macro that we have provided as part of Hands-on codes. Simple addition of two nonsquare matrices.ĬUDA Runtime functions : Different types of Memory Write a program to demonstrate use of CUDA Synchronous and CUDA Asynchronous APIs with CUDA streams for Write CUDA program to compute Matrix-Vector multiplication using Synchronus execution and Write a program to demonstrate use of CUDA Synchronous and CUDA Asynchronous APIs with CUDA streamsįor simple addition of two square matrices. Write a CUDA program to add two vectors using Multiple streams. Write a CUDA program to add the values of two array and print the Write Cuda Program to perform cuda_malloc_test using Page-lock Memory. Write Cuda Program to perform cuda_malloc_test using Pageble host Memory. Important topics on CUDA C Runtime and "Streams - Asynchronous Concurrent Execution" are discussed in detail with example programs CUDA Streams several ways in which execution of certain operations simultaneously on the single and multiple GPUs. On present GPUs, the task parallelism based application kernels is growing, and the state-of-art GPUs provide an opportunity for programmers to extract even more speed from GPU-based implementations. In data parallelism, computing the same function on lots of data elements is done and in task parallelism, two or more completely different tasks in parallel. On GPUs, massively data-parallel computations can be performed and task parallelism involving multi-threaded CPU applications can also be performed on modern GPUs. Efficient CUDA programs exploit both thread parallelism within a thread block and coarser block parallelism across thread blocks.
Hypack 2018 software#
NVIDIA's software CUDA Programming model automatically manages the threads and it is significantly differs from single threaded CPU code and to some extent even the parallel code. | Module 7:CUDA enabled NVIDIA GPU Streams : Concurrent Ashynchronous Execution | Module 6:CUDA enabled NVIDIA GPU Memory Optimization Programs - Tuning & Performance | Module 5:CUDA enabled NVIDIA GPU Programs - Application Kernels | Module 4:CUDA enabled NVIDIA GPU Programs using BLAS libraries for Matrix Computations | Module 3: CUDA enabled NVIDIA GPU Programs on Num. | Module 2:Getting Started :PGI OpenACC APIs on CUDA enabled NVIDIA GPU Overview | Module 1: Getting Started:CUDA enabled NVIDIA GPU Programs Coprocessor GPUs Cluster Applications Reg
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