If you have a long running kernel or set of kernels. In this.1 answer Top answer: The difference in total time is due to the fact that work is launched to the GPU asynchronously. pytorch 1.0.1 p圓.6_cuda10.0.130_cudnn7.4. In general, the API calls section lets you know what the CPU is doing, while, the profiling results tells you what the GPU is doing. GCC version: (Ubuntu 5.4.0-6ubuntu1~16.04.11) 5.4.0 20160609 nvprof Command-line profiler Current command-line profiler still available Profiling Session NVIDIA Visual Profiler Timeline GPU/CPU Timeline CPU Timeline CUDA API Invocations GPU Timeline Device Activity Measuring Time Measure time with horizontal rulers. 4692 Warning: This can happen if device ran out of memory or if a device kernel was stopped due to an assertion.
R executes the GPU solver and exits as normal, and nvprof in another console captures the GPU behavior and prints the. D:\Programing\CudaTest\圆4\Debug>nvprof CudaTest 4692 NVPROF is profiling process 4692, command: CudaTest Hello World from GPU 4692 Profiling application: CudaTest 4692 Warning: Found 23 invalid records in the result. 223.33us 223.33us 223.33us cudaLaunch 0.02 72.438us 1 72.438us 72.438us 72.438us. Use nvprof as a wrapper to launch R by typing nvprof R, then run the GPU solver and exit, or use nvprof to launch R with a batch script: nvprof R CMD BATCH script.R Launch nvprof -profile-all-processes in a separated console. This seems wrong, am I misusing the API or is there some other problem? jacobi1-rowmaj 28141 NVPROF is profiling process 28141, command. Opening nvvp, I see that the kernels runing on the 5 streams one after the other, instead of all at the same time. usr/local/cuda/bin/nvprof -concurrent-kernels on -print-api-summary -print-gpu-summary -output-profile profile.nvvp -f -profile-from-start off -track-memory-allocations on -demangling on -trace gpu,api python stream.py Running nvprof with the following command:
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