GPU-Accelerated Solving

GPU-Accelerated Solving#

Warning

This feature is experimental and not tested in CI due to the lack of GPU-enabled machines. Use with caution and please report any issues.

Linopy supports GPU-accelerated optimization solvers that can significantly speed up solving large-scale linear programming problems by leveraging the parallel processing capabilities of modern GPUs.

Supported GPU Solvers#

cuPDLPx#

cuPDLPx is an open-source, GPU-accelerated first-order solver developed by MIT. It implements a Primal-Dual hybrid gradient (PDHG) method optimized for GPUs.

To install it, you have to have the CUDA Toolkit installed requiring NVIDIA GPUs on your computer. Then, install with

# Install CUDA Toolkit first (if not already installed)
# Follow instructions at: https://developer.nvidia.com/cuda-downloads

# Install cuPDLPx
pip install cupdlpx>=0.1.2

Features:

  • GPU-accelerated solving for large-scale linear programs

  • Open source (Apache 2.0 license)

  • Direct API integration with linopy

  • Designed for problems with millions of variables and constraints

Limitations:

  • Currently supports only Linear Programming (LP)

  • Does not support Mixed-Integer Programming (MIP) or Quadratic Programming (QP)

  • Lower numerical precision compared to CPU solvers (typical tolerance: ~2.5e-4 vs 1e-5)

  • File I/O not currently supported through cuPDLPx API

For a complete list of cuPDLPx parameters, see the cuPDLPx documentation.

Xpress with GPU Acceleration#

FICO Xpress version 9.8 and later includes GPU acceleration support for certain operations.

Features:

  • Commercial solver with GPU support

  • Supports LP, MIP, and QP

  • Full-precision solving

Prerequisites#

Hardware Requirements#

GPU solvers require:

  • NVIDIA GPU with CUDA support (compute capability 6.0 or higher recommended)

  • Sufficient GPU memory for your problem size (varies by problem)

  • PCIe 3.0 or higher for optimal data transfer

Software Requirements#

  1. CUDA Toolkit: Most GPU solvers require CUDA 11.0 or later

  2. Compatible GPU drivers: Match your CUDA version

Verifying Installation#

To verify that the GPU solvers are properly installed and detected:

import linopy
from linopy.solver_capabilities import (
    SolverFeature,
    get_available_solvers_with_feature,
)

# Check available solvers
print("All available solvers:", linopy.available_solvers)

# Check GPU-accelerated solvers
gpu_solvers = get_available_solvers_with_feature(
    SolverFeature.GPU_ACCELERATION, linopy.available_solvers
)
print("GPU solvers:", gpu_solvers)

By default, GPU tests are skipped in the test suite to support CI environments without GPUs. To run GPU tests locally:

# Run all tests including GPU tests
pytest --run-gpu

# Run only GPU tests
pytest -m gpu --run-gpu

# Run specific GPU test
pytest test/test_optimization.py -k cupdlpx --run-gpu

References#