Lately NVIDIA added repository for Ubuntu 12.04 and Ubuntu 14.04. Recently I hit problem with missing dependencies for libcheese-gtk23 and libcheese7 libraries while installing latest CUDA 6.5 on clean Ubuntu 14.04. Remedy to this can be found on askubuntu. So, the complete set of commands is presented below.
Makefiles are quite straightforward and easy to write (in reasonable situations). But GNU Make is not crossplafrom. CMake is cross-platform, cross-application (it can generate projects for different IDEs and Makefile itself).
It also allows you to split source directory and directory with intermediate files and compiled binary. Now CMake natively supports CUDA.
I’d like to show how to use HPC part written on C++ with CUDA in Python code. So, every heavy part may be done on GPU with CUDA, all gluing tasks (with beautiful matplotlib plots) are done on CPU with Python.
It is a good tone to check CUDA API errors while calling cudaMalloc() and other functions. It also helps to find floating bugs caused by hardware (lack of memory, etc). I provide below an adapted version of CudaSafeCall I found many weeks ago in the Internet. Simply remove
#define CUDA_ERROR_CHECK in production if unneeded.