CUDA C is an extenstion to ANSI C that NVIDIA has created to compile programs containing CUDA kernel code into executables that can use the GPU on a target device.
Please read the LC GPU Technology Web page to familiarize yourself with the LLNL GPU setup.

Machines and Versions: CUDA C is available everywhere the CUDA Toolkit is installed. See LC graphics software page. There are no GPUs on the login nodes. Onlyl batch nodes have GPUs.
Location: The CUDA toolkits are located in /opt/cudatoolkit* . To make one available, you must load the cudatoolkit module for the version desired. The following command loads version7.5 of the CUDA toolkit, placing nvcc in your PATH and set LD_LIBRARY_PATH to run the resulting binaries correctly:
module load cudatoolkit/7.5
Available modules can be shown by typing
module avail cuda
and module details can be shown by typing, e.g.,
module show cudatoolkit/7.5
Settings: None required beyond loading the module.
To write a CUDA C program, you create a sourcefile that has the .cu extension and compile it using the nvcc compiler, which has similar semantics to gcc and other compilers. The resulting binary will contain code to run on the GPU. Example:
nvcc myfile.cu -o myprogram
Debugging with NVVP: To run nvvp, you need to be explicit about choosing the right java JRE on our clusters:
module load java/1.8.0
If you do not do this, then you will very likely get a java error.
For help with the CUDA C API, see the Help section below.

See NVIDIA's developer zone for more information about CUDA programming.
Another good resource is this CUDA tutorial:http://llpanorama.wordpress.com/cuda-tutorial/
Recommended reading: CUDA By Example by Sanders and Kandrot.
Help is available from the lc-hotline@llnl.gov, (925) 422-4531.

You can download GPU Computing SDK code samples at the NVIDIA developer site.