What is the canonical way to check for errors using the CUDA runtime API?

Probably the best way to check for errors in runtime API code is to define an assert style handler function and wrapper macro like this:

#define gpuErrchk(ans) { gpuAssert((ans), __FILE__, __LINE__); }
inline void gpuAssert(cudaError_t code, const char *file, int line, bool abort=true)
{
   if (code != cudaSuccess) 
   {
      fprintf(stderr,"GPUassert: %s %s %d\n", cudaGetErrorString(code), file, line);
      if (abort) exit(code);
   }
}

You can then wrap each API call with the gpuErrchk macro, which will process the return status of the API call it wraps, for example:

gpuErrchk( cudaMalloc(&a_d, size*sizeof(int)) );

If there is an error in a call, a textual message describing the error and the file and line in your code where the error occurred will be emitted to stderr and the application will exit. You could conceivably modify gpuAssert to raise an exception rather than call exit() in a more sophisticated application if it were required.

A second related question is how to check for errors in kernel launches, which can’t be directly wrapped in a macro call like standard runtime API calls. For kernels, something like this:

kernel<<<1,1>>>(a);
gpuErrchk( cudaPeekAtLastError() );
gpuErrchk( cudaDeviceSynchronize() );

will firstly check for invalid launch argument, then force the host to wait until the kernel stops and checks for an execution error. The synchronisation can be eliminated if you have a subsequent blocking API call like this:

kernel<<<1,1>>>(a_d);
gpuErrchk( cudaPeekAtLastError() );
gpuErrchk( cudaMemcpy(a_h, a_d, size * sizeof(int), cudaMemcpyDeviceToHost) );

in which case the cudaMemcpy call can return either errors which occurred during the kernel execution or those from the memory copy itself. This can be confusing for the beginner, and I would recommend using explicit synchronisation after a kernel launch during debugging to make it easier to understand where problems might be arising.

Note that when using CUDA Dynamic Parallelism, a very similar methodology can and should be applied to any usage of the CUDA runtime API in device kernels, as well as after any device kernel launches:

#include <assert.h>
#define cdpErrchk(ans) { cdpAssert((ans), __FILE__, __LINE__); }
__device__ void cdpAssert(cudaError_t code, const char *file, int line, bool abort=true)
{
   if (code != cudaSuccess)
   {
      printf("GPU kernel assert: %s %s %d\n", cudaGetErrorString(code), file, line);
      if (abort) assert(0);
   }
}