100.0 80,098 1 80,098.0 80,098.0 80,098 80,098 0.0 vector_add(float *, float *, float *, int) CUDA Memory Operation Statistics (by time): Time (%) Total Time (ns) Count Avg (ns) Med (ns) Min (ns) Max (ns) StdDev (ns) Operation --- --- --- --- --- --- --- -...
100.0 670,516,888 1 670,516,888.0 670,516,888.0 670,516,888 670,516,888 0.0 vector_add(float *, float *, float *, int) CUDA Memory Operation Statistics (by time): Time (%) Total Time (ns) Count Avg (ns) Med (ns) Min (ns) Max (ns) StdDev (ns) Operation --- --- ---...
可以使用nsys 工具分析。 enerating CUDA Memory Operation Statistics... CUDA Memory Operation Statistics (nanoseconds) Time(%) Total Time Operations Average Minimum Maximum Name --- --- --- --- --- --- --- 78.8 42212544 2304 18321.4 2751 109728 [CUDA Unified Memory memcpy HtoD] 21.2 11349...
首先程序的输入为x = torch.randn(1823, 781, device='cuda'),也就是需要读写的数据应该是1823*781*4/1024/1024=5.43MB,算上一些local memory读写数据大致是符合预期的,这里我们看不出什么端倪。需要指出的是,我们可以从这个图里面观测自己的kernel从Device Memory读写的数据是否正常来判断程序的优化是否生效。
memory is filled in.cudaIpcOpenEventHandle(&readyIpcEvent, readyIpcEventHandle);//import the allocation. The operation does not block on the allocation being ready.cudaMemPoolImportPointer(&ptr, importedMemPool, importData);//Wait for the prior stream operations in the allocating...
Statistics... Generating CUDA Memory Operation Statistics... CUDA Kernel Statistics (nanoseconds) Time(%) Total Time Instances Average Minimum Maximum Name --- --- --- --- --- --- --- 100.0 3360 2 1680.0 1664 1696 conv_forward_kernel CUDA Memory Operation Statistics (nanoseconds) Time(...
getting device pointer, allocating and freeing memory Methods of array class Get information about the array object. Move and Reorder array content reorder, transpose, flip, join, tile, etc. 4 Functions to work with internal array layout
memory access Instruction optimization NVIDIA Confidential Amdahl's Law – Example P = parallel proportion N = number of procs S= Assume N → infinity Only ¾ of program can be parallelized S=4 Unoptimized: Optimized: Parallel Serial Parallel The maximum speedup can only be 4x NVIDIA ...
2.3.3 Memory Hierarchy The memory model of CUDA is tightly related to its thread bathing mechanism. There are several kinds of memory spaces on the device: • Read-write per-thread registers • Read-write per-thread local memory • Read-write per-block shared memory • Read-write per...
5.5. Asynchronous SIMT Programming Model In the CUDA programming model a thread is the lowest level of abstraction for doing a computation or a memory operation. Starting with devices based on the NVIDIA Ampere GPU architecture, the CUDA programming model provides acceleration to memory operations ...