HiGHS on a GPU
From HiGHS v1.10.0, its first order primal-dual LP (PDLP) solver cuPDLP-C can be run on an NVIDIA GPU under Linux and Windows. However, to achieve this, CUDA utilities must be installed and HiGHS must be built locally using CMake, as described below.
PDLP: A health warning
First order solvers for LP are still very much "work in progress". Although impressive results have been reported, these are often to lower accuracy than is achieved by simplex and interior point solvers, have been obtained using top-of-the-range GPUs, and not achieved for all problem classes. Note that, due to PDLP using internal scaling and relative termination conditions, a solution deemed optimal by PDLP may not be accepted as optimal by HiGHS. The user should consider the infeasibility data returned by HighsInfo to decide whether the solution is acceptable to them.
If you use the HiGHS PDLP solver, in the first instance it is recommended that you increase the primal and dual feasibility tolerances to 1e-4, since these are the default values used natively by cuPDLP-C, and will result in the algorithm terminating much sooner.
Requirements
CUDA Toolkit and CMake.
A CUDA Toolkit installation is required, along with the matching NVIDIA driver. Please install both following the instructions on NVIDIA's website.
HiGHS must be build locally with CMake.
Make sure the CUDA compiler nvcc is installed by running
nvcc --versionBuild HiGHS with GPU support
HiGHS must be built, from the root directory, with
cmake -S. -Bbuild -DCUPDLP_GPU=ON
cmake --build build --parallelThis uses FindCUDAToolkit to find a CUDA installation locally.
Find CUDA
If CUDA is not found automatically, there is an extra option -DCUPDLP_FIND_CUDA=ON, to be used with -DCUPDLP_GPU=ON, which instead uses cuPDLP-C's FindCUDAConf.cmake.
This requires the environment variable CUDA_HOME to be set to the directory with the CUDA installation. Having set this, run
cmake -S. -Bbuild -DCUPDLP_GPU=ON -DCUPDLP_FIND_CUDA=ON
cmake --build build --parallelto build HiGHS.