How do you properly handle errors in CUDA runtime API?

Other Questions API
1

I’ve been working on a CUDA project and I’m not sure about the best way to deal with errors. People often say you should check the status of every API call, but I’m not clear on how to do this efficiently.

The CUDA documentation mentions functions like cudaGetLastError, cudaPeekAtLastError, and cudaGetErrorString. But I’m confused about how to use these together to catch and report errors without making my code super messy.

Can someone explain a good method for error handling in CUDA? I want to make sure my code is reliable, but I don’t want to add tons of extra lines just for error checking. Is there a standard approach that most CUDA developers use?

Here’s a simple example of what I’m doing now, but I’m not sure if it’s the best way:

cudaError_t err = cudaMalloc(&d_data, size);
if (err != cudaSuccess) {
    printf("Error: %s\n", cudaGetErrorString(err));
    return -1;
}

Is this okay, or is there a better method? Any advice would be really helpful!

2

I’ve been doing CUDA development for a while, and I’ve found that a good approach is to create a custom error-handling class. This allows for more flexibility and control over how errors are handled and reported.

Here’s a basic idea of what I mean:

class CudaError {
public:
    static void check(cudaError_t err, const char* file, int line) {
        if (err != cudaSuccess) {
            fprintf(stderr, "CUDA error at %s:%d: %s\n", file, line, cudaGetErrorString(err));
            cudaDeviceReset();
            exit(EXIT_FAILURE);
        }
    }
};

#define CUDA_CHECK(err) CudaError::check(err, __FILE__, __LINE__)

Then you can use it like this:

CUDA_CHECK(cudaMalloc(&d_data, size));

This approach gives you the ability to customize error handling (like logging to a file instead of stderr) and provides more context about where the error occurred. It’s served me well in larger projects where more detailed error information is crucial for debugging.

3

Error handling in CUDA is indeed crucial for robust code. I’ve found that using a combination of cudaGetLastError() and cudaDeviceSynchronize() after kernel launches works well. This approach catches both synchronous and asynchronous errors.

For API calls, your current method is sound, but consider creating a utility function for error checking. This can centralize error handling logic and make your main code cleaner. Something like:

void checkCudaErrors(cudaError_t result) {
if (result != cudaSuccess) {
fprintf(stderr, “CUDA error: %s\n”, cudaGetErrorString(result));
exit(-1);
}
}

Then you can simply call checkCudaErrors(cudaMalloc(&d_data, size)); throughout your code. This approach balances thoroughness with readability.

4

hey there! i’ve dealt with this before. one trick i use is wrapping error checks in a macro. something like:\n\n#define CUDA_CHECK(call) { cudaError_t err = call; if (err != cudaSuccess) { printf("CUDA error: %s\n", cudaGetErrorString(err)); exit(-1); } }\n\nthen u can just do CUDA_CHECK(cudaMalloc(&d_data, size)); way cleaner imo