Cudalaunch nvprof
nvprof -print-gpu-trace python trainmnist.py Visual Profiler On GPU machine, run nvprof -o prof.nvvp python trainmnist.py Copy prof.nvvp into your local machine scp yourgpumachine:/path/to/prof.nvvp. Can someone help me figure out the reason? Thanks.ĬUDA version: 8. nvvp.md Usually, located at /usr/local/cuda/bin Non-Visual Profiler nvprof python trainmnist.py I prefer to use -print-gpu-trace. For each kernel, there is 256 threads in a block and 5 blocks in a grid.Īll buffers used for memcpy are pinned, I am sure that I have meet those requirements for overlapping kernel execution and data transfers. You can see none operations in streams are overlapped. I attached the timeline of processing 10 tasks. The problem is that operations in different streams are not overlapping. The main pipeline logic is in the following. And after the last stage, I will record an event then will go to process next task.
CUDALAUNCH NVPROF CODE
The host code will first chooses a Channel, an encapsulation of scratchpad memory and CUDA objects, when it process a task. either CUDA LAUNCH BLOCKING1 for GPUs using CUDA or OMP NUM THREADS1 for. Each task will go through the same pipeline with different input data. nvprof and Nsight profilers, researchers capture the executed GPU kernels. testthrust 2903 Profiling result: Type Time() Time Calls Avg Min Max Name GPU activities: 56.61 4.3840us 3 1.4610us 1.2480us 1.8560us CUDA memcpy HtoD 43.39 3.
![cudalaunch nvprof cudalaunch nvprof](https://files.speakerdeck.com/presentations/c4210f2fa76e4a59abeb690cf39d3d8c/slide_48.jpg)
testthrust D0 99 D1 88 2903 Profiling application.
![cudalaunch nvprof cudalaunch nvprof](https://files.speakerdeck.com/presentations/c4210f2fa76e4a59abeb690cf39d3d8c/slide_46.jpg)
My program is a pipeline, which contains multiple kernels and memcpys. /testthrust H has size 4 H0 14 H1 20 H2 38 H3 46 H now has sNVPROF is profiling process 2903, command. 1 14.842us 14.842us 14.842us cudaLaunch 0.00 5.2530us 1 5.2530us 5.2530us.