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GPU-acceleration routines for DifferentialEquations.jl
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.appveyor.yml first version Jun 24, 2019



This library is a component package of the DifferentialEquations.jl ecosystem. It includes functionality for making use of GPUs in the differential equation solvers.

Within-Method GPU Parallelism with Direct CuArray Usage

The native Julia libraries, including (but not limited to) OrdinaryDiffEq, StochasticDiffEq, and DelayDiffEq, are compatible with u0 being a CuArray. When this occurs, all array operations take place on the GPU, including any implicit solves. This is independent of the DiffEqGPU library. These speedup the solution of a differential equation which is sufficiently large or expensive.

Parameter-Parallelism with GPU Ensemble Methods

Parameter-parallel GPU methods are provided for the case where a single solve is too cheap to benefit from within-method parallelism, but the solution of the same structure (same f) is required for very many different choices of u0 or p. For these cases, DiffEqGPU exports the following ensemble algorithms:

  • EnsembleGPUArray: Utilizes the CuArray setup to parallelize ODE solves across the GPU.
  • EnsembleCPUArray: A test version for analyzing the overhead of the array-based parallelism setup.

For more information on using the ensemble interface, see the DiffEqDocs page on ensembles

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