Are there GPU libraries for solving sparse linear systems?
The libraries with GPU support that I know are listed below, for the “usual” CPU solvers see Libraries for solving sparse linear systems. As for the custom matrix-vector product, one of the main issues with these libraries is that each uses their own representation of sparse matrices.
Which is the best way to solve L V?
Which means that I endup trying to solve a system: L v = i where L is at least a 1000×1000 matrix where each row has only 9 non-zeros. So, it’s really really sparse. I have tried solving it using SoPlex, LaPACK and SuperLU (these last 2 through Armadillo ). But all are too slow. On a 10000×10000 the best I have is 18s.
Can a Solver Library work with a vector product?
RELATED SIDE QUESTION: Many of the solver libraries can work with an user defined matrix-vector-product which is the basic operation of any iterative solver.
Which is the best software to use for sparse solvers?
I would recommend SuiteSparse or cuSPARSE. Try Eigen? basic advice, I know. Eigen has added several sparse solvers in the last time, and also offers benchmarks with established methods such as UMFPACK or SUPERLU against which it seems to perform quite well.
Can a GPU be used for linear algebra?
Built upon efficient GPU representations of scalar values, vectors, and matrices, such a package can implement high-performance linear algebra operations such as vector-vector and matrix-vector operations. In this section, we describe in more detail the internal representation of linear algebra operators in our GPU linear algebra library.
How to solve linear PDEs on the GPU?
To solve linear PDEs on the GPU, we need a linear algebra package. Built upon efficient GPU representations of scalar values, vectors, and matrices, such a package can implement high-performance linear algebra operations such as vector-vector and matrix-vector operations.
How to solve ax = b in sparse linear algebra?
Use BIConjugate Gradient STABilized iteration to solve Ax = b. Use Conjugate Gradient iteration to solve Ax = b. Use Conjugate Gradient Squared iteration to solve Ax = b. gmres (A, b [, x0, tol, restart, maxiter, M, …])