A heat conduction benchmark, part of the mantevo project.

TeaLeaf is a mini-app that solves the linear heat conduction equation on a spatially decomposed regularly grid using a 5 point stencil with implicit solvers. TeaLeaf currently solves the equations in two dimensions, but three dimensional support is in beta.

In TeaLeaf temperatures are stored at the cell centres. A conduction coefficient is calculated that is equal to the cell centred density or the reciprocal of the density. This is then averaged to each face of the cell for use in the solution. The solve is carried out using an implicit method due to the severe timestep limitations imposed by the stability criteria of an explicit solution for a parabolic partial differential equation. The implicit method requires the solution of a system of linear equations which form a regular sparse matrix with a well defined structure.

The computation in TeaLeaf has been broken down into "kernels", low level building blocks with minimal complexity. Each kernel loops over the entire grid and updates the relevant mesh variables. Control logic within each kernel is kept to a minimum, allowing maximum optimisation by the compiler. Memory is sacrificed in order to increase performance, and any updates to variables that would introduce dependencies between loop iterations are written into copies of the mesh.

Normally, third party solvers are used to invert the system of linear equations, because of the complexity of state of the art methods. For reference the simplest iterative method, the Jacobi method, has been included in the reference version as the default as well the option to use a Conjugate Gradient or a Chebyshev solver. These does have the advantage of being matrix free and independent of library dependencies. By deault a pre-conditioner is not invoked, but a simple pre-conditioner is available as an option which just uses diagonal scaling. Note that this simple method will not always speed up the solve time.

The solvers have been written in Fortran with OpenMP and MPI and they have also been ported to OpenCL to provide an accelerated capability.

Other versions invoke third party linear solvers and currently include Petsc, Trilinos and Hypre, which are in beta release. For each of these version there are instructions on how to download, build and link in the relevant library.

For more information, please see the documentiation included in the repository.

This is the reference release of the code. This version features both MPI and OpenMP as default.

This is the GPU implementation using the OpenCL programming language.

This is the GPU implementation using the CUDA programming language.

This implementation makes use of the HYPRE library for the linear solver. The validity of this implementation is untested.

This implementation makes use of the PETSc library for the linear solver. The validity of this implementation is untested.

This implementation makes use of the Trilinos library for the linear solver. The validity of this implementation is untested.