SELF is a Fortran-based library that provides data-structure and type-bound routines for implementing Spectral Element Methods in one, two, and three spatial dimensions. Routines are currently available for collocation and discontinuous galerkin formulations on unstructured meshes with isoparametric elements.
The SELF API is designed based on the assumption that SEM developers and researchers need to be able to implement derivatives in 1-D and divergence, gradient, and curl in 2-D and 3-D on scalar, vector, and tensor functions using spectral collocation, continuous galerkin, and discontinuous galerkin spectral element methods. Additionally, as we enter the exascale era, we are currently faced with a zoo of compute hardware that is available. Because of this, SELF routines provide support for GPU acceleration through AMD's HIP and support for multi-core, multi-node, and multi-GPU platforms with MPI.
SELF-Fluids leverages SELF to solve the Compressible Navier-Stokes equations on multi-GPU architectures.
The Fluid class employs the Nodal Discontinuous Galerkin Spectral Element method for the spatial discretization. The inter-element fluxes are numerically estimated using the upwind Lax-Friedrich's approximate Riemman solver. Currently, the model is forward-stepped using Williamson's 3rd Order Low-Storage Runge-Kutta time integrator. There are plans to develop and support additional explicit and implicit time-stepping schemes.
SELF-Fluids currently exists on the stale/self-fluids branch of the SELF repository. SELF is actively being refactored to support GPU platforms from multiple hardware vendors. We expect SELF-Fluids to be fully functional on Multi-GPU platforms by August 2021.
SELF runs on AMD GPUs
April 16, 2021
HIPFort Migration Complete
February 11, 2021
SELF has been isolated into it's own repository at https://github.com/FluidNumerics/SELF
Serial CPU only and single GPU routines have been implemented and tested.
CI infrastructure is in place for automatically testing serial and single GPU tests.
Docker containers are available on Google Cloud Platform and Dockerhub
Multi-GPU ( MPI + HIPFort ) and MPI-Only Support
CGNS/HOPr File IO Support
Implement Demo Applications
Compressible 2-D Navier Stokes
Shallow Water Equations
Refactoring and HIP-Fortran Migration
May 1, 2020
In Q2-Q3 of this year, we are working on the following tasks on feature branches of the self-fluids repository
Transition CUDA-Fortran to HIP-Fortran
Switch namelist input for JSON input
Enhance automated testing and CI capabilities
You can track our progress on these tasks through the SELF-Fluids Github site
The roadmap below shows the features we plan on implementing with a timeline for the expected completion.
New SELF API Specification
Routines defined by the cross product of
[ interpolation, differentiation, divergence, gradient, curl ] X [scalar, vector, tensor] X [1D, 2D,3D] X [Strong Form, DG Weak Form, CG Weak Form ] x [Serial, Single GPU (HIP), MPI-only, MPI+HIP (multi-gpu) ]
All routines for Serial CPU and Single GPU implemented
MPI-only support for all routines
HOPr file IO support
VTK file IO support
SpecMesh file IO support
MPI+HIP support for all routines
OBJ file IO support
CGNS (with higher order extension) file IO support
Nektar++ file IO support
Example Applications : SELF-Fluids (Compressible Navier-Stokes)
Board of Directors formation campaign
Formally establish board of direction & operating policies
Formalize Specifications & Contributing guidelines