Simple library to help the Deep Learning frameworks manage CUDA memory.

CNMeM is not intended to be a general purpose memory management library. It was designed as a simple tool for applications which work on a limited number of large memory buffers.

CNMeM is mostly developed on Ubuntu Linux. It should support other operating systems as well. If you encounter an issue with the library on other operating systems, please submit a bug (or a fix).

CNMeM relies on the CUDA toolkit. It uses C++ STL and the Pthread library on Linux. On Windows, it uses the native Win32 threading library. The build system uses CMake. The unit tests are written using Google tests (but are not mandatory).

The CUDA toolkit is required. We recommend using CUDA >= 7.0 even if earlier versions will work.

• Download from the CUDA website
• Follow the installation instructions
• Don't forget to set your path. For example:
  • CUDA_HOME=/usr/local/cuda
  • LD_LIBRARY_PATH=$CUDA_HOME/lib64:$LD_LIBRARY_PATH

% cd $HOME
% git clone https://github.com/NVIDIA/cnmem.git cnmem

% cd cnmem
% mkdir build
% cd build
% cmake ..
% make

To build the tests, you need to add an extra option to the cmake command.

% cd cnmem
% mkdir build
% cd build
% cmake -DWITH_TESTS=True ..
% make

The source folder contains a header file 'include/cnmem.h' and the build directory contains the library 'libcnmem.so', 'cnmem.lib/cnmem.dll' or 'libcnmem.dylib', depending on your operating system.