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) The idea is that each thread loads to shared memory the pixels that correspond to their location, and the threads close to the border of each thread block also take care of loading the neighbouring pixels from other blocks (the 'apron' part of the convolution) It seems to be working, but its hard to tell at glance if there is a subtle mistake.
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. The memory deep dive series: Part 1: Memory Deep Dive Intro. This topic amongst others will be covered in the upcoming FVP book.
In case of an NVIDIA GPU, the shared memory, the L1 cache and the Constant memory cache are within the streaming multiprocessor block. This is a series of articles that I wrote to share what I learned while documenting memory internals for large memory server configurations. 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. Part 2 - CUDA Kernels and their Launch Parameters This post details the CUDA memory model and is the fourth part in the CUDA series.