TrixiCUDA.jl offers CUDA acceleration for solving hyperbolic PDEs.
Update on Nov 21, 2024:
- Due to the issue from upstream with Trixi.jl and CUDA.jl in Julia v1.11, this package now supports only Julia v1.10. Using or developing this package with Julia v1.11 will result in precompilation errors. To fix this, downgrade to Julia v1.10. If you have any other problems, please file issues here.
Update on Oct 30, 2024:
- The general documentation is now available at https://trixi-gpu.github.io (in development).
- Documentation specific to this package can be found at https://trixi-gpu.github.io/TrixiCUDA.jl/dev (in development).
Update on Sep 16, 2024:
- GPU-related tests are run now locally rather than on CI. Once the repository is ready to publish, we will set up JuliaGPU CI infrastructure to run tests on a system with a GPU using JuliaGPU Buildkite.
The package is now in pre-release status and will be registered once the initial release version is published. We want to make sure most key features are ready and optimizations are done before we roll out the first release.
Users who are interested now can install the package by running the following command in the Julia REPL:
julia> using Pkg; Pkg.add(url="https://github.com/trixi-gpu/TrixiCUDA.jl.git")Then the package can be used with the following simple command:
julia> using TrixiCUDAThis package serves as a support package, so it is recommended to these packages together:
julia> using Trixi, TrixiCUDA, OrdinaryDiffEqDevelopers can start their development by first forking and cloning the repository to their terminal.
Then enter the Julia REPL in the package directory, activate and instantiate the environment by running the following command:
julia> using Pkg; Pkg.activate("."); Pkg.instantiate()Our current focus is on the semidiscretization of PDEs. The table below shows the status of this work across different mesh types and solvers. Looking ahead, we plan to extend parallelization to include mesh initialization and callbacks on the GPU.
| Mesh Type | Spatial Dimension | Solver Type | Status |
|---|---|---|---|
TreeMesh |
1D, 2D, 3D | DGSEM |
✅ Supported |
StructuredMesh |
1D, 2D, 3D | DGSEM |
🛠️ In Development |
UnstructuredMesh |
2D | DGSEM |
🟡 Planned |
P4estMesh |
2D, 3D | DGSEM |
🟡 Planned |
DGMultiMesh |
1D, 2D, 3D | DGMulti |
🟡 Planned |
Let's take a look at a simple example to see how to use TrixiCUDA.jl to run the simulation on the GPU (now only CUDA-compatible).
# Take 1D linear advection equation as an example
using Trixi, TrixiCUDA
using OrdinaryDiffEq
###############################################################################
# semidiscretization of the linear advection equation
advection_velocity = 1.0
equations = LinearScalarAdvectionEquation1D(advection_velocity)
solver = DGSEM(polydeg = 3, surface_flux = flux_lax_friedrichs)
coordinates_min = -1.0
coordinates_max = 1.0
mesh = TreeMesh(coordinates_min, coordinates_max,
initial_refinement_level = 4,
n_cells_max = 30_000)
semi = SemidiscretizationHyperbolic(mesh, equations, initial_condition_convergence_test,
solver)
###############################################################################
# ODE solvers, callbacks etc.
ode = semidiscretizeGPU(semi, (0.0, 1.0)) # from TrixiCUDA.jl
summary_callback = SummaryCallback()
analysis_callback = AnalysisCallback(semi, interval = 100)
save_solution = SaveSolutionCallback(interval = 100,
solution_variables = cons2prim)
stepsize_callback = StepsizeCallback(cfl = 1.6)
callbacks = CallbackSet(summary_callback, analysis_callback, save_solution,
stepsize_callback)
###############################################################################
# run the simulation
sol = solve(ode, CarpenterKennedy2N54(williamson_condition = false),
dt = 1.0, save_everystep = false, callback = callbacks)
summary_callback()Please check benchmark branch and this part will be updated soon.
We always welcome new contributors to join us in future development. Please feel free to reach out if you would like to get involved!