Here we are getting just the device pointer using cudaHostGetDevicePointer function and not allocating a new memory for device. _global_ void array_sum (int *d_a, int *d_b, int *d_c, int size) memset(h_c2, 0, NO_BYTES) ĭevice allocation syntax: int *d_a2, *d_b2, *d_c2 // cudaHostGetDevicePointer((int **)&d_a2, (int *)h_a2, 0) cudaHostGetDevicePointer((int **)&d_b2, (int *)h_b2, 0) cudaHostGetDevicePointer((int **)&d_c2, (int *)h_c2, 0) Let’s take an example to discuss further. GPU kernel launch, and data initialization and transfer happens from the CPU. GPU Execution modelĪs discussed in Part 1 of this series, GPU is a co-processor. Hence they are faster than the L2 cache, and GPU RAM. In case of an NVIDIA GPU, the shared memory, the L1 cache and the Constant memory cache are within the streaming multiprocessor block. As the distance of the memory increases from the processor, the data access from that memory take more clock cycles to process. Spatial locality - the tendency to access the memory locations with in a relatively close proximity to the currently accessed location.ĭue to the existence of this principle, any computer architecture will have a hierarchy of memory, thereby optimizing the execution of the instructions. Temporal locality - the tendency to access the same memory location repeatedly with in a relatively short period of time. There are two types of locality - temporal locality, spatial locality. This phenomenon is called principle of locality. Part 3 - GPU Device Architecture Memory Hierarchyĭuring the execution of a computer application, more often the instructions have the tendency to access the same set of memory locations repeatedly over a short period of time. We allocate space in the device so we can copy the input of the kernel ( a & b) from the host to the device. This includes device memory allocation and deallocation as well as data transfer between the host and device memory. Part 2 - CUDA Kernels and their Launch Parameters A CUDA application manages the device space memory through calls to the CUDA runtime. This post details the CUDA memory model and is the fourth part in the CUDA series.
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