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CUDA, Supercomputing for the Masses: Part 3

Congratulations! Thanks to Part 1 and Part 2 of this series on CUDA (short for "Compute Unified Device Architecture"), you are now a CUDA-enabled programmer with the ability to create and run programs that can use many hundreds of simultaneous threads on CUDA-enabled devices. In Part 2,, I provided a working example of a pervasive CUDA application pattern -- move data to device, run one or mores kernels to perform a calculation, and retrieve the result(s). Essentially, can be morphed into whatever application you desire simply by substituting your own kernel and loading your own data (which is what I do for the example in this column). Subsequent columns will discuss CUDA asynchronous I/O and streams.

"You now know enough to be dangerous!" is a humorous and accurate way to summarize the previous paragraph. The good news about CUDA is that it provides a natural way to translate your thoughts as a programmer into massively-parallel programs. The bad news is that more understanding is required to make those programs both robust and efficient.

Don't be cautious. Start experimenting and go for it! CUDA provides both programming tools and constructs to create excellent software and the only way to really learn it is to experiment. In reality, these columns will supplement your experimentation and learning process by highlighting CUDA features through short examples and by bringing good sources of information on the Internet to your attention. Remember that the CUDA Zone is a great place for all things CUDA and the forums are a great place to look for answers to your questions, plus they have the advantage of being interactive so you can post questions and get answers.

This and the next few columns utilize a simple array reversal application to expand your knowledge and highlight the performance impact of shared memory. I discuss error checks and performance behavior, along with the CUDA profiling tool. I've also included the source listing for the next column so you can see how to implement array reversal with shared memory. The program implements an obvious, yet low performance, way to reverse an array in global memory on a CUDA device. Do not use it as a model for your applications because global memory is not the best memory type to use for this application -- plus this version also performs uncoalesced memory accesses, which adversely affects global memory performance. The best global memory bandwidth is achieved when simultaneous memory accesses can be coalesced into a single memory transaction. In subsequent columns, I discuss the differences between global and shared memory as well as the various requirements for memory accesses to coalesce based on the compute capability of the device.

CUDA Error Handling

Detecting and handling errors is essential to creating robust and usable software. Users tend to get very grumpy when their applications fail or produce incorrect results. For developers, adding error-handling code can be annoying, and tedious. It can clutter up the elegance of the code, and slow the development process in attempting to deal with every conceivable error. Yes, error handling is a thankless job but keep in mind you are not doing it for yourself (although I have been saved countless times through good error checking) -- rather it is being done for the people who are going to use the program. If something can fail, users need to know why it failed and, more importantly, what they can do to fix the problem. Enough said, good error-handling and recovery can really make your application a hit with the users. Commercial developers should especially take note.

The CUDA designers are aware of the importance of good error handling. To facilitate this, every CUDA call -- with the exception of kernel launches -- returns an error code of type cudaError_t. Upon successful completion, cudaSuccess is returned. Otherwise, an error code is returned.

A human-readable description of the error can be obtained from:

char *cudaGetErrorString(cudaError_t code);

C-language programmers will recognize a similarity between this method and the C library, which uses the variable errno to indicate errors and the reporting of human-readable error messages with perror and strerror. The C library paradigm has worked well for many millions of lines of C-code, and there is no doubt it should work well in the future for CUDA software.

CUDA also provides a method, cudaGetLastError, which reports the last error for any previous runtime call in the host thread. This has multiple implications:

  • The asynchronous nature of the kernel launches precludes explicitly checking for errors with cudaGetLastError.
  • Instead, use cudaThreadSynchronize which blocks until the device has completed all previous calls, including kernel calls, and returns an error if one of the preceding tasks fails. Queuing multiple kernel launches unfortunately implies that error checking can only be done after all the kernels have completed -- unless explicit error checking and reporting to the host is performed by programmers within the kernel.
  • Errors are reported to the correct host thread. If the host is running multiple threads, as might be the case when an application is using multiple CUDA devices, the error will be reported to the correct host thread.
  • When multiple errors occur between calls to cudaGetLastError, only the last error will be reported. This means the programmer must take care to tie the error to the runtime call that generated the error or risk making an incorrect error report to users.

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