In CUDA, Supercomputing for the Masses: Part 13 of this article
series, I resumed the discussion of "texture memory" began in
Part 11 of this series and included information such as the
recently introduced CUDA Toolkit 2.2 that added ability to write
to global memory on the GPU that has a 2D texture bound to it.
This installment focuses on debugging techniques and how CUDA-
GDB can be used to effectively diagnose and debug CUDA code --
with an emphasis on how to speed the process when looking
through large amounts of data as well as the thread syntax and
semantic differences needed to debug kernels while they are
running on the GPU. Upfront, youo need to be aware of two
important points about the current state of CUDA-GDB: - It only
runs on UNIX-based systems. Microsoft users in particular should
look to the recently announced Visual Studio debugger, which
I'll cover in a future column. - X11 cannot be running on the
GPU that is used for debugging. Instead use either a multi-GPU
system or kill X11 and remotely