GPUs with compute capability 8.6 support shared memory capacity of 0, 8, 16, 32, 64 or 100 KB per SM. Threads copy the data from global memory to shared memory with the statement s[t] = d[t], and the reversal is done two lines later with the statement d[t] = s[tr]. For exponentiation using base 2 or 10, use the functions exp2() or expf2() and exp10() or expf10() rather than the functions pow() or powf(). NVIDIA shall have no liability for the consequences or use of such information or for any infringement of patents or other rights of third parties that may result from its use. For example, the NVIDIA Tesla V100 uses HBM2 (double data rate) RAM with a memory clock rate of 877 MHz and a 4096-bit-wide memory interface. Threads on a CPU are generally heavyweight entities. To view a librarys install name, use the otool -L command: The binary compatibility version of the CUDA libraries on Windows is indicated as part of the filename. If the shared memory array size is known at compile time, as in the staticReverse kernel, then we can explicitly declare an array of that size, as we do with the array s. In this kernel, t and tr are the two indices representing the original and reverse order, respectively. It can be simpler to view N as a very large number, which essentially transforms the equation into \(S = 1/(1 - P)\). On parallel systems, it is possible to run into difficulties not typically found in traditional serial-oriented programming. Answer: CUDA has different layers of memory. Incorrect or unexpected results arise principally from issues of floating-point accuracy due to the way floating-point values are computed and stored. In the next post I will continue our discussion of shared memory by using it to optimize a matrix transpose. The one exception here is when multiple threads in a warp address the same shared memory location, resulting in a broadcast. This technique could be used when the data dependency is such that the data can be broken into chunks and transferred in multiple stages, launching multiple kernels to operate on each chunk as it arrives. Exponentiation With Small Fractional Arguments, 14. Reproduction of information in this document is permissible only if approved in advance by NVIDIA in writing, reproduced without alteration and in full compliance with all applicable export laws and regulations, and accompanied by all associated conditions, limitations, and notices. If L1-caching is enabled on these devices, the number of required transactions is equal to the number of required 128-byte aligned segments. Other company and product names may be trademarks of the respective companies with which they are associated. What's the difference between CUDA shared and global memory? Memory instructions include any instruction that reads from or writes to shared, local, or global memory. When we can, we should use registers. 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. In this case, multiple broadcasts from different banks are coalesced into a single multicast from the requested shared memory locations to the threads. As the stride increases, the effective bandwidth decreases until the point where 32 32-byte segments are loaded for the 32 threads in a warp, as indicated in Figure 7. The following documents are especially important resources: In particular, the optimization section of this guide assumes that you have already successfully downloaded and installed the CUDA Toolkit (if not, please refer to the relevant CUDA Installation Guide for your platform) and that you have a basic familiarity with the CUDA C++ programming language and environment (if not, please refer to the CUDA C++ Programming Guide).