Installation Instructions for MCX

1. System Requirements

We aim to port MCX for all modern GPUs. Currently, we have two MCX editions, the CUDA-edition is supported only on the NVIDIA graphics card, and an OpenCL-edition, supported for all OpenCL-compliant computing devices (Intel/AMD CPUs, NVIDIA GPU and AMD GPUs, etc). In order to run MCX (CUDA-edition), you have to make sure you have the right hardware and software support, this includes:

a CUDA enabled graphics card made by NVIDIA
the latest graphics driver for the GPU, update from NVIDIA's website.

Starting from v0.9, MCX/MCXLAB released packages are compiled with CUDA library statically linked (embedded). Therefore, you do not need to install CUDA library in order to run MCX/MCXLAB.

You must use a CUDA capable NVIDIA graphics card in order to use MCX. A list of CUDA capable cards can be found at here. The oldest graphics card that MCX supports is the "Fermi" series (circa 2010). Using the latest NVIDIA card is expected to produce the best speed. The default release of MCX supports atomic operations and photon detection within a single binary. In the below webpage, we summarized the speed differences between different generations of NVIDIA GPUs

http://mcx.space/gpubench/

Windows users: if you use MCXStudio, please follow this video tutorial (Lesson 6) to apply a registry fix to enable MCX to run more than 5 seconds on your computer. Alternatively, you may use the Windows installer to install MCX. This is important!

http://mcx.space/wiki/index.cgi/wiki/index.cgi?Demo/MCXStudio#Lesson_6_Fix_Unspecified_launch_failure_error_on_Windows_11_minutes

2. Download and Install

To install MCX, you need to download the binary executable compiled for your computer architecture (32 or 64bit) and platform, extract the package and run the executable under the <mcx root>/bin directory.

3. Testing hardware support

To test if your system supports MCX, you need to open a terminal (for Windows users, please click on the Start menu and type "cmd", then enter), and type

 cd "path/to/mcx/bin"

where path/to/mcx/bin should be replaced to the actual extracted bin folder from the downloaded package. If your path contains space, you must enclose it with double-quotes.

Once you are in the bin/ folder, you can then type

 mcx -L

You may see 3 possible outcomes

1. mcx successfully prints at least 1 supported GPU on your system - to this end, MCX is ready to use
2. mcx shows the below error

 No CUDA-capable GPU device found

 MCX ERROR(-1):No GPU device found
  in unit mcextreme.c:54

If your are certain your system has a working NVIDIA GPU, it is likely that your GPU driver was not up to date or was not installed properly. Please check your driver installation again.

3. mcx gives an error or pops up an error window showing that some libraries (*.dll on Windows, or *.so on Linux, or *.dylib on Mac) are missing. If the missing library is a CUDA library, that means the binary was not compiled with static-linking and you must download the library file separately. You may google this missing file, and download it and place it inside your system folder.

In the event that your system does not have supported GPU, you may consider using MCX-CL instead - MCX-CL has the same functionality as MCX but it can also be executed on AMD/Intel CPUs and GPUs.

4. Testing hardware support for MCXLAB

If you use MCXLAB, you may use the below procedure to test if it can use your GPU. First, open MATLAB or octave, then type

 addpath('/path/to/mcxlab/')

so that mcx.mex* file is in the search path. You may type

 which mcx

 which mcxlab

to confirm it is added to MATLAB. Then you can use the below command to query your GPUs

 info=mcxlab('gpuinfo')

You must see valid output from this call, otherwise, your system does not have GPU or it is not properly configured to run MCXLAB.

5. Running MCX with GUI

If you are used to graphics interfaces, you can find an executable mcxstudio under the same folder as mcx. MCX Studio is a cross-platform interface to interact with MCX. It gives users a straightforward way to set various of parameters for a MCX session, and organize different sessions into projects that can be saved for later use. Please read README to see how to use this tool to create, run and save the simulation sessions.

6. Compiling MCX from source code

If you want to compile an executable on your machine from the source code, you should follow the steps described below:

To compile MCX/MCXLAB, you need to install CUDA toolkit (version 7-10 is supported). Once you install the CUDA library, you need to setup the system environment variable in order to let the compiler find the needed CUDA run-time library. For Linux and Mac OS users, you might need to add the following settings to your shell initialization file. Use "echo $SHELL" command to identify your shell type. For c-shell, i.e. csh/tcsh, add the following lines to your ~/.cshrc file

  if ("uname -p" =~ "*_64" ) then
          setenv LD_LIBRARY_PATH "/usr/local/cuda/lib64"
  else
          setenv LD_LIBRARY_PATH "/usr/local/cuda/lib"
  endif
  setenv PATH "/usr/local/cuda/bin:$PATH"

and for "bash" or "sh" users, add

  if [[ "uname -p" =~ .*_64 ]]; then
          export LD_LIBRARY_PATH="/usr/local/cuda/lib64"
  else
          export LD_LIBRARY_PATH="/usr/local/cuda/lib"
  fi
  export PATH="/usr/local/cuda/bin:$PATH"

to your ~/.bash_profile. If the libcudart.so* file is installed in a non-standard folder, please replace the paths in the above scripts to your installation path where libcudart.so resides.

6.1. Download the source code

You need to first download the source code from the Download page.

6.2. Install the compilers

You need install the CUDA and C compilers on your system, this includes

1. CUDA compiler and run-time libraries: this will be installed when you download and install the CUDA driver/sdk/tools from this link
2. GNU toolchain: you need to have gcc and make commands on your system. Usually
- for Debian-based Linux users, you can get these basic compiling tools by a single command:
```
sudo apt-get install build-essential
```
  and then type your own password
- for Redhat-based systems:
```
su -c 'yum groupinstall "Development Tools"'
```
  and then type the root password
- for Windows users, you need to install MinGW, a minimum Unix system on Windows, the automatic-installer can be downloaded from here.
- for Mac OS users, the GNU compilers can be installed from the Xcode tools from this link
3. For Windows, you also need to install Microsoft Visual C++ 200x Express Edition (or the full version; the Express edition is free. MCX was tested with VC++2005).

6.3. Setup paths

For Linux/Mac OS users, you need to make sure you have followed the instructions to setup the paths described in the "Running MCX" section.

For Windows, one need to add the path to nvcc (by default, C:\CUDA\bin), path to cl.exe (by default, C:\Program Files\Microsoft Visual Studio 8\VC\bin) and path to gcc/make (by default, C:\MinGW\bin) to your Path environment variable. You can follow the screenshots as in this tutorial, and paste the following string at the beginning of the variable value field.

 C:\Program Files\Microsoft Visual Studio 8\VC\bin;C:\MinGW\bin;C:\CUDA\bin;

If you have installed VC2008, you need to replace "Microsoft Visual Studio 8" to "Microsoft Visual Studio 9".

If you have installed CUDA, MSVC or MinGW at a non-standard location, please open the Makefile under <mcx>/src to update all the library/include directories to the actual path.

6.4. make your binary

For Linux/Mac OS, you need to open an terminal, "cd" to <mcx>/src, and type "make". This will compile MCX with the default option, i.e. non-atomic memory write, LL5 RNG and fast-math library. You can "cat Makefile" and see other options. If everything is working ok, you should see a binary file generated under <mcx>/bin.

For Windows, you need to double click on a shortcut named "MSYS", a terminal will pop up. Then type "cd /path/to/your/mcx/src", then type "make". The binary will be generated as <mcx>/bin/mcx.exe.