Massively Parallel Solvers
Designed to take full advantage of today's most powerful petascale supercomputers
Parallel sparse linear solver
ESPRESO framework contains several highly scalable solvers based on the methods of domain decomposition which allows the computational capacity of the state-of-the-art supercomputers to be fully utilized and thus solve problems with billions of unknowns.
Strong scalability - elasticity problem
300 Million unknowns Jet Engine case
Successfully Tested on large Peta-scale machines
Largest problem solved by ESPRESO in 160 seconds
Hybrid solver strong scalability
20 billion unknowns
11 bilion unknowns
We also offer our massively parallel solver as a third party library. To interface the ESPRESO with the third party software, a general API has been implemented. The API has been successfully used to connect the ESPRESO with the Elmer. Even though the ESPRESO is C++ library, the API uses plain C only. Hence, it is easy to use it with various other languages such as Fortran
Scalable nonlinear solvers
For nonlinear problems with equality and inequality conditions, we implement in-house solver based on quadratic programming problems. This solver in combination with domain decomposition techniques brings to users a possibility to solve contact problems in parallel with almost linear complexity
semi-monotonic augmented Lagrangian method with separable convex constraints and general equality constraints
modified proportioning with the reduced gradient projection
In-house constrained QP algorithm
We combine augmented Lagrangian based solver with active set reflecting restarted preconditioned conjugate gradients in inner loop, and FETI based domain decomposition techniques. By this we are able to solve especially large contact problems efficiently on HPC infrastructures.
Available third party solvers
For highly heterogenous systems, algebraic multigrid or direct solvers can be used for solving defined problems.