I’ve been working with CUDA and noticed that many developers recommend checking the return status of every API call for errors. The CUDA runtime API provides several functions for error handling like cudaGetLastError, cudaPeekAtLastError, and cudaGetErrorString. However, I’m wondering what’s the most efficient approach to combine these functions together? I want to create a reliable error checking system that can catch and display errors without having to write tons of repetitive code for each API call. What would be the standard practice for implementing this kind of error handling in CUDA applications?
The main difference: cudaGetLastError clears the error after you check it, while cudaPeekAtLastError leaves it alone. I usually go with cudaGetLastError since it automatically resets the flag. For kernel launches, you’ve got to call cudaDeviceSynchronize or cudaStreamSynchronize first, then check for errors. Kernels run async, so there’s nothing to catch until you sync up. I handle this with two different approaches - synchronous API calls give you error codes right away, but kernels need that explicit sync step before you can check anything. This split approach has saved me tons of debugging time in complex apps where timing issues hide the real problems.
honestly, the simplest approach i’v found is creating a wrapper function that takes the cuda call as a param and handles all the error checking internally. way cleaner than macros imo, and you can add custom logging or whatever you need. also, don’t forget about async errors - those won’t show up until you sync.
I’ve had great success with a custom error checking macro in production CUDA code. I define CUDA_CHECK(call) that wraps API calls and handles all the error checking automatically. The macro checks the return value and uses cudaGetErrorString to grab a readable error message when things go wrong. This keeps your main code clean while making sure every API call gets checked properly. Just make sure your macro includes file name and line number info - you’ll thank yourself later when debugging. This catches runtime errors way more reliably than manually checking each call, especially when you’re dealing with hundreds of CUDA API calls.