These instructions assume that you have an NVIDIA or AMD GPU in your system. While modern Intel GPUs support OpenCL, their double-precision performance is unlikely to bring speedup compared to a modern CPU.
Ubuntu
Make sure you have make, g++ 4.9.3 or higher (or clang++ 6.0 or higher).
If using a NVIDIA device, install the NVIDIA CUDA toolkit and clinfo tool.
apt update
apt install nvidia-cuda-toolkit clinfo
If you have an AMD GPU, install the Radeon Open Computer platform (ROCm). Instructions can be found here.
Windows
Make sure you have mingw32-make and g++ 4.9.3 or higher. If not, you can install both as part of the Rtools 4.0 suite. During installation make sure that the 64-bit toolchain is installed. Upon installing RTools 4.0, open the RTools bash and enter pacman -Sy mingw-w64-x86_64-make. This will install mingw32-make. Add C:\RTools40\usr\bin and C:\RTools40\mingw64\bin to the PATH environment variable if they are not added automatically.
In order to make sure both mingw32-make and g++ are installed, try out the mingw32-make --version and g++ --version commands.
If you have an NVIDIA device, install the latest NVIDIA CUDA toolkit found here. AMD users can use the OCL-SDK.
In order to run an OpenCL application, you need to specify the platform and device IDs of your target device. If you only have a single OpenCL-enabled device (most likely a GPU) both IDs are 0. In this case you can proceed to Step 3.
If you have installed OpenCL runtimes (drivers) for multiple GPUs or have previously installed the OpenCL runtime for your CPU, you first need to determine the platform and device ID of your target device. You can use the clinfo tool to list all OpenCL-enabled devices and their IDs. On Linux clinfo can be obtained using sudo apt install clinfo or equivalently using yum. You can also build the tool from the source which is available at here.
Windows binaries of clinfo are available here.
On Windows also identify the location of the OpenCL.lib file. If using an NVIDIA GPU, the file is typically located at C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v10.2/lib/x64 or similar. If using an AMD GPU and the previously suggested OCL-SDK, the file is located at C:/Program Files (x86)/OCL_SDK_Light/lib/x86_64.
Install CmdStanR by running
# install the pacakges required to install via Github
install.packages("remotes")
# cmdstanr requires the unreleased
remotes::install_github("jgabry/posterior")
remotes::install_github("stan-dev/cmdstanr")
# install cmdstan
library(cmdstanr)
install_cmdstan()
Upon installing also write the location of the OpenCL.lib file to the Cmdstan make/local file. Replace the value of path_to_opencl_lib with the correct location and run the following:
path_to_opencl_lib <- "C:/Program Files (x86)/OCL_SDK_Light/lib/x86_64"
write(paste0("LDFLAGS= -L\"",path_to_opencl_lib,"\" -lOpenCL"), file.path(cmdstan_path(), "make", "local"), append = TRUE)Open lr_glm_cmdstanr.R, set the OpenCL IDs and run the script.