GPU acceleration

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 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.

Termination criteria

Although the PDLP solver may report that it has terminated with an optimal solution, HiGHS may identify that the solution returned by PDLP is not optimal. As discussed in HiGHS feasibilty and optimality tolerances, this is due to PDLP using relative termination criteria.

If you use the HiGHS PDLP solver, in the first instance it is recommended that you increase the feasibility and optimality tolerances to 1e-4, since this will result in the algorithm terminating much sooner. There are multiple feasibility and optimality tolerances, but all will be set to the value of the kkt_tolerance option (if it differs from its default value of 1e-4) so this is recommended in the first instance.

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 --version

Build HiGHS with GPU support

HiGHS must be built, from the root directory, with

cmake -S. -Bbuild -DCUPDLP_GPU=ON
cmake --build build --parallel

This 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 --parallel

to build HiGHS.