GaspiLS is a scalable linear solver library for the exascale age and is industry proven in CFD and FEM simulations. Its easily extendable standard API is compact, yet powerful for parallel computations and allows for smooth transitions from legacy applications in little to no time.

GaspiLS comes with a collection of iterative solvers (Richardson, (P)CG, BiPCGStab, GMRES) and preconditiones (Jacobi, ILU(0), MILU), which can be easily expanded by adding custom solvers and/or preconditioners.

A hybrid-parallel implementation for scalability results in a much better surface to volume ratio and the task based parallelization yields optimal load balancing. The design is multi-threaded with thread-safe matrix assembly and fully threaded matrix finalization.

GaspiLS inherits the Gaspi/GPI-2 programming model and leverages its unique advantages to achieve overlap of computation and communication.

Preconditioners are designed to improve the convergence of iterative Krylov subspace solvers, i.e. they reduce the number of required iterations or are even a necessary condition to solve a given problem at all. Therefore, beside the asolute performance and scalability of the basic solvers provided by GapiLS, preconditioners are a key component to solve sparse linear systems efficiently.

GaspiLS provides two classes of black-box preconditioners, now. Algebraic Multigrid (AMG) for symmetric positive definite problems as they appear e.g. in CFD applications and Multi-Level ILU factorizations as a generic preconditioner. Both implementations follow a scalable, efficient, hybrid parallel Gaspi based approach.

AMG, as representative of a multigrid method, has a linear computational complexity, which is optimal. It can be applied to symmetric positive definite problems. In AMG, a hierarchy of operators is directly constructed from the linear system, without explicit knowledge of the underlying geometry or partial differential equation.

Multi-Level ILU, on the other hand, is widely used because of its robustness, accuracy, and usability as a black-box preconditioner for general sparse linear systems. ILU consists of LU based Gaussian elimination combined with dropping. Depending on the chosen input parameters and the amount of available resources, Multi-Level ILU allows for seamless interpolation between full Gaussian elimination on one end (exact inverse, high resource consumption) and Jacobi preconditioning on the other end (low resource consumption, low quality). Our implementation supports row and col based permutation
in the factorization which is often needed to solve a linear system with ill-conditioned matrices.