Within each iteration of the for loop, a value in shared memory is broadcast to all threads in a warp. Because the memory copy and the kernel both return control to the host immediately, the host function cpuFunction() overlaps their execution. This should be our first candidate function for parallelization. Applying Strong and Weak Scaling, 6.3.2. This is particularly beneficial to kernels that frequently call __syncthreads(). Failure to do so could lead to too many resources requested for launch errors. Missing dependencies is also a binary compatibility break, hence you should provide fallbacks or guards for functionality that depends on those interfaces. Bandwidth is best served by using as much fast memory and as little slow-access memory as possible. In a typical system, thousands of threads are queued up for work (in warps of 32 threads each). The CUDA Runtime handles kernel loading and setting up kernel parameters and launch configuration before the kernel is launched. Once we have located a hotspot in our applications profile assessment and determined that custom code is the best approach, we can use CUDA C++ to expose the parallelism in that portion of our code as a CUDA kernel. Figure 6 illustrates how threads in the CUDA device can access the different memory components. Useful Features for tex1D(), tex2D(), and tex3D() Fetches, __launch_bounds__(maxThreadsPerBlock,minBlocksPerMultiprocessor), Using the CUDA Occupancy Calculator to project GPU multiprocessor occupancy, cudaOccupancyMaxActiveBlocksPerMultiprocessor, // When the program/library launches work, // When the program/library is finished with the context, Table 5. if several threads had accessed the same word or if some threads did not participate in the access), the full segment is fetched anyway. When using the driver APIs directly, we recommend using the new driver entry point access API (cuGetProcAddress) documented here: CUDA Driver API :: CUDA Toolkit Documentation. There's no way around this. When choosing the first execution configuration parameter-the number of blocks per grid, or grid size - the primary concern is keeping the entire GPU busy. This approach will tend to provide the best results for the time invested and will avoid the trap of premature optimization. Refer to the CUDA Toolkit Release Notes for details for the minimum driver version and the version of the driver shipped with the toolkit. When JIT compilation of PTX device code is used, the NVIDIA driver caches the resulting binary code on disk. nvidia-smi is targeted at Tesla and certain Quadro GPUs, though limited support is also available on other NVIDIA GPUs. However, bank conflicts occur when copying the tile from global memory into shared memory. In order to maintain binary compatibility across minor versions, the CUDA runtime no longer bumps up the minimum driver version required for every minor release - this only happens when a major release is shipped. These accept one of three options:cudaFuncCachePreferNone, cudaFuncCachePreferShared, and cudaFuncCachePreferL1. Results obtained using double-precision arithmetic will frequently differ from the same operation performed via single-precision arithmetic due to the greater precision of the former and due to rounding issues.
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