Let's create a sample CUDA code first:
#include <stdio.h>
#include <cuda_runtime.h>
__global__ void add(int a, int b, int *c)
{
*c = a + b;
}
int main()
{
int a = 1, b = 2, c;
int *dev_c;
cudaMalloc(&dev_c, sizeof(int));
add<<<1, 1>>>(a, b, dev_c);
cudaMemcpy(&c, dev_c, sizeof(int), cudaMemcpyDeviceToHost);
printf("%d + %d is %d\n", a, b, c);
cudaFree(dev_c);
return 0;
}
Compiling:
$ nvcc sample.cu -o sample
Calling with nvprof:
$ nvprof ./sample
==72071== NVPROF is profiling process 72071, command: ./sample
1 + 2 is 3
==72071== Profiling application: ./sample
==72071== Profiling result:
Type Time(%) Time Calls Avg Min Max Name
GPU activities: 56.32% 3.4240us 1 3.4240us 3.4240us 3.4240us add(int, int, int*)
43.68% 2.6560us 1 2.6560us 2.6560us 2.6560us [CUDA memcpy DtoH]
API calls: 99.08% 165.86ms 1 165.86ms 165.86ms 165.86ms cudaMalloc
0.82% 1.3800ms 101 13.663us 80ns 732.85us cuDeviceGetAttribute
0.05% 81.893us 1 81.893us 81.893us 81.893us cudaFree
0.02% 38.522us 1 38.522us 38.522us 38.522us cudaMemcpy
0.01% 20.007us 1 20.007us 20.007us 20.007us cudaLaunchKernel
0.01% 9.0370us 1 9.0370us 9.0370us 9.0370us cuDeviceGetName
0.00% 6.5120us 1 6.5120us 6.5120us 6.5120us cuDeviceGetPCIBusId
0.00% 1.4030us 3 467ns 281ns 831ns cuDeviceGetCount
0.00% 572ns 2 286ns 121ns 451ns cuDeviceGet
0.00% 220ns 1 220ns 220ns 220ns cuDeviceTotalMem
0.00% 160ns 1 160ns 160ns 160ns cuDeviceGetUuid
Here we can see the performed calls directly on stdout. Let's do it again, but using a GUI instead:
$ nvprof -o sample.nvvp ./sample
==74063== NVPROF is profiling process 74063, command: ./sample
1 + 2 is 3
==74063== Generated result file: /home/talles/GitHub/books-notebooks/02 - Fashion MNIST/sample.nvvp
The 99% of time spent by cudaMalloc pops out immediatelly when inspecting the profiling visually.
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