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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