![]() ![]() Support host page-locked memory mapping: Yesĭevice supports Unified Addressing (UVA ): Yes Total number of registers available per block: 65536 Warp size: 32 Maximum number of threads per multiprocessor: 2048 Maximum number of threads per block: 1024 Max dimension size of a thread block (x,y,z ): ( 1024, 1024, 64 ) Max dimension size of a grid size (x,y,z ): ( 2147483647, 65535, 65535 ) Maximum memory pitch: 2147483647 bytesĬoncurrent copy and kernel execution: Yes with 7 copy engine (s ) Run time limit on kernels: No Total shared memory per multiprocessor: 98304 bytes Total amount of shared memory per block: 49152 bytes Total amount of constant memory: 65536 bytes Maximum Layered 2D Texture Size, (num ) layers 2D =( 32768, 32768 ), 2048 layers GPU Max Clock rate: 1380 MHz ( 1.38 GHz ) Memory Clock rate: 877 Mhz Total amount of global memory: 32510 MBytes ( 34089730048 bytes ) ( 080 ) Multiprocessors, ( 064 ) CUDA Cores/MP: 5120 CUDA Cores Cores, Schedulers and Streaming MultiprocessorsĬUDA Device Query (Runtime API ) version (CUDART static linking )ĭevice 0: "Tesla V100-PCIE-32GB" CUDA Driver Version / Runtime Version 11.5 / 11.4ĬUDA Capability Major/Minor version number: 7.0 Unlike global memory, there is no penalty for strided access of shared memory. One use of shared memory is to extract a 2D tile of a multidimensional array from global memory in a coalesced fashion into shared memory, and then have contiguous threads stride through the shared memory tile. ![]() Shared memory is an on-chip memory shared by all threads in a thread block. ![]() We can handle these cases by using a type of CUDA memory called shared memory. When accessing multidimensional arrays it is often necessary for threads to index the higher dimensions of the array, so strided access is simply unavoidable. Threads can access data in shared memory loaded from global memory by other threads within the same thread block. Shared memory is allocated per thread block, so all threads in the block have access to the same shared memory. In fact, shared memory latency is roughly 100x lower than uncached global memory latency (provided that there are no bank conflicts between the threads). Has the lifetime ofīecause the shared memory is on-chip, it is much faster than local and global memory. Has the lifetime of theĪpplication - it is persistent between kernel launches.Ī potential performance gotcha, it resides in global memory and can be 150x slower Accessible from either the host or device. Potentially 150x slower than register or shared memory - watch out for uncoalesced Accessible by any thread of the block from which it was created.Īccessible by all threads. Is only accessible by the thread.Ĭan be as fast as a register when there are no bank conflicts or when reading from The fastest form of memory on the multi-processor. _device_ is optional when used with _local_, _shared_, or _constant_ Automatic variables without any qualifier reside in a register – Except arrays that reside in local memory.Variable Type Qualifiers Variable declaration Nvprof # command-line CUDA profiler (logger)Ĭomputeprof # CUDA profiler (with GUI) from nvidia-visual-profiler package Cuda Memory Model ![]() Some light-weight utils are also available: NVIDIA Nsights Systems allows for in depth analyze of and application. An early step of kernel performance analysis should be to check occupancy and observe the effects on kernel execution time when running at different occupancy levels. When occupancy is at a sufficient level to hide latency, increasing it further may degrade performance due to the reduction in resources per thread. Low occupancy results in poor instruction issue efficiency, because there are not enough eligible warps to hide latency between dependent instructions. Occupancy is defined as the ratio of active warps (a set of 32 threads) on an Streaming Multiprocessor (SM) to the maximum number of active warps supported by the SM. Performance Tuning - grid and block dimensions for CUDA kernels For example (32,32,1) creates a block of 1024 threads. This is the product of whatever your threadblock dimensions are (x*y*z). The maximum number of threads in the block is limited to 1024. x // This variable contains the block index within the grid in x-dimension. x // This variable contains the number of threads per block in x-dimension. x // This variable contains the thread index within the block in x-dimension. Performance Tuning - grid and block dimensions for CUDA kernelsĬores, Schedulers and Streaming Multiprocessors ![]()
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