If all threads of a warp access the same location, then constant memory can be as fast as a register access. For example, improving occupancy from 66 percent to 100 percent generally does not translate to a similar increase in performance. NVIDIA accepts no liability for inclusion and/or use of NVIDIA products in such equipment or applications and therefore such inclusion and/or use is at customers own risk. A diagram depicting the timeline of execution for the two code segments is shown in Figure 1, and nStreams is equal to 4 for Staged concurrent copy and execute in the bottom half of the figure. Register dependencies arise when an instruction uses a result stored in a register written by an instruction before it. aims to make the expression of this parallelism as simple as possible, while simultaneously enabling operation on CUDA-capable GPUs designed for maximum parallel throughput. Consequently, the order in which arithmetic operations are performed is important. However, as with APOD as a whole, program optimization is an iterative process (identify an opportunity for optimization, apply and test the optimization, verify the speedup achieved, and repeat), meaning that it is not necessary for a programmer to spend large amounts of time memorizing the bulk of all possible optimization strategies prior to seeing good speedups. This spreadsheet, shown in Figure 15, is called CUDA_Occupancy_Calculator.xls and is located in the tools subdirectory of the CUDA Toolkit installation. High Priority: Avoid different execution paths within the same warp. The --ptxas options=v option of nvcc details the number of registers used per thread for each kernel. By default the 48KBshared memory setting is used. Memory optimizations are the most important area for performance. Using asynchronous copies does not use any intermediate register. Medium Priority: Use shared memory to avoid redundant transfers from global memory. Sequential copy and execute and Staged concurrent copy and execute demonstrate this. To target specific versions of NVIDIA hardware and CUDA software, use the -arch, -code, and -gencode options of nvcc. The device will record a timestamp for the event when it reaches that event in the stream. The CUDA Toolkit libraries (cuBLAS, cuFFT, etc.) Because shared memory is shared by threads in a thread block, it provides a mechanism for threads to cooperate. NVIDIA Corporation (NVIDIA) makes no representations or warranties, expressed or implied, as to the accuracy or completeness of the information contained in this document and assumes no responsibility for any errors contained herein. To use dynamic linking with the CUDA Runtime when using the nvcc from CUDA 5.5 or later to link the application, add the --cudart=shared flag to the link command line; otherwise the statically-linked CUDA Runtime library is used by default. Theoretical bandwidth can be calculated using hardware specifications available in the product literature. Now, if 3/4 of the running time of a sequential program is parallelized, the maximum speedup over serial code is 1 / (1 - 3/4) = 4. Constant memory used for data that does not change (i.e. Devices to be made visible to the application should be included as a comma-separated list in terms of the system-wide list of enumerable devices.
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cuda shared memory between blocks