This time is captured for kernel in the ‘GPU activities’. This time is captured for the Launch API like cuLaunchKernel in the ‘API Calls’ section.Įventually kernel starts execution on the GPU and runs to the completion. It returns immediately, before the kernel has completed, and perhaps before the kernel has even started. And timing information here represents the execution time on the host.įor example, CUDA kernel launches are asynchronous from the point of view of the CPU. Section ‘API Calls’ list CUDA Runtime/Driver API calls. And timing information here represents the execution time on the GPU. Section ‘GPU activities’ list activities which execute on the GPU like CUDA kernel, CUDA memcpy, CUDA memset. What is the difference between ‘GPU activities’ and ‘API calls’ in the results of ‘nvprof’? program # command-line CUDA profiler (logger) Some light-weight utils are also available:Ĭomputeprof # CUDA profiler (with GUI) from nvidia-visual-profiler package 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. 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 Some frequently used commands/qualifiers/concepts are listed below for convenience. PL: Ta strona nie jest tłumaczona na polski.ĬUDA is an acronym for “Compute Unified Device Architecture”.Cores, Schedulers and Streaming Multiprocessors
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