By Shane Cook
If you want to benefit CUDA yet don't have event with parallel computing, CUDA Programming: A Developer's creation offers an in depth consultant to CUDA with a grounding in parallel basics. It starts off by way of introducing CUDA and bringing you up to the mark on GPU parallelism and undefined, then delving into CUDA set up. Chapters on center suggestions together with threads, blocks, grids, and reminiscence concentrate on either parallel and CUDA-specific concerns. Later, the e-book demonstrates CUDA in perform for optimizing functions, adjusting to new undefined, and fixing universal problems.
Read Online or Download CUDA Programming: A Developer's Guide to Parallel Computing with GPUs (Applications of GPU Computing Series) PDF
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Additional info for CUDA Programming: A Developer's Guide to Parallel Computing with GPUs (Applications of GPU Computing Series)
It can also be performed in a somewhat restricted way through atomic operations to or from global memory. CUDA splits problems into grids of blocks, each containing multiple threads. The blocks may run in any order. Only a subset of the blocks will ever execute at any one point in time. A block must execute from start to completion and may be run on one of N SMs (symmetrical multiprocessors). Blocks are allocated from the grid of blocks to any SM that has free slots. Initially this is done on a round-robin basis so each SM gets an equal distribution of blocks.
As CPUs contain multiple levels of cache, this brings the data into the device. Typically the L3 cache is shared by all cores. Thus, the memory access from the first fetch is distributed to all cores in the CPU. By contrast in the second case, four separate memory fetches are needed and four separate L3 cache lines are utilized. The latter approach is often better where the CPU cores need to write data back to memory. Interleaving the data elements by core means the cache has to coordinate and combine the writes from different cores, which is usually a bad idea.
Fork/join pattern The fork/join pattern is a common pattern in serial programming where there are synchronization points and only certain aspects of the program are parallel. The serial code runs and at some point hits a section where the work can be distributed to P processors in some manner. It then “forks” or spawns N threads/processes that perform the calculation in parallel. These then execute independently and finally converge or join once all the calculations are complete. This is typically the approach found in OpenMP, where you define a parallel region with pragma statements.