Best practices for error handling in CUDA runtime API?

Other Questions API
1

Hey CUDA developers! I’ve been digging into error handling for CUDA runtime API calls. It seems like checking every API call’s return status is the way to go, but I’m not sure about the best approach.

I know there are functions like cudaGetLastError, cudaPeekAtLastError, and cudaGetErrorString. But how do you use these effectively? I want to catch and report errors without bloating my code too much.

Anyone have a good strategy or pattern they use? Maybe a simple wrapper function or macro? I’m looking for something that’s reliable but doesn’t clutter up the main logic too much.

Thanks in advance for any tips or best practices you can share!

2

I’ve been working with CUDA for a few years now, and error handling is definitely crucial. Here’s what I’ve found works well:

I created a simple macro that wraps CUDA calls and checks for errors. It looks something like this:

#define CUDA_CHECK(call) {
cudaError_t err = call;
if (err != cudaSuccess) {
fprintf(stderr, “CUDA error in %s at line %d: %s\n”,
FILE, LINE, cudaGetErrorString(err));
exit(EXIT_FAILURE);
}
}

Then I use it like: CUDA_CHECK(cudaMalloc(&d_data, size));

This approach catches errors immediately, pinpoints the location, and doesn’t clutter the main code too much. It’s saved me countless hours of debugging.

For kernel launches, I usually add a cudaDeviceSynchronize() right after and wrap that in the macro too. This catches any asynchronous errors that might occur during kernel execution.

Hope this helps! Let me know if you want more details on my error handling strategy.

3

hey there! i’ve had some success with error handling in cuda. one thing that works for me is using a simple inline function like this:

inline void cudaCheckError(const char *file, int line) {
cudaError_t err = cudaGetLastError();
if (err != cudaSuccess) {
printf(“CUDA error at %s:%d: %s\n”, file, line, cudaGetErrorString(err));
exit(-1);
}
}

then just call it after each cuda operation. keeps things clean and catches most issues.

4

From my experience, a robust error handling strategy for CUDA involves a combination of techniques. I’ve found success using a custom error-checking function that wraps CUDA calls and logs errors to a file. This approach allows for detailed error tracking without cluttering the main code.

For kernel launches, I implement a post-launch check using cudaGetLastError() followed by cudaDeviceSynchronize(). This catches both synchronous and asynchronous errors.

In performance-critical sections, I sometimes use cudaPeekAtLastError() to check for errors without introducing synchronization overhead.

Remember to handle errors gracefully - sometimes recovering from an error is preferable to program termination, depending on your application’s requirements.

Ultimately, the key is consistency in your error-checking approach across your codebase.