Mechanical APDL Basic Analysis Guide - Ansys

Solver
Typical Applications
* In total means the sum of all processors.
Note
Ideal Model
Size
Memory Use
memory than
the sparse
solver.
Disk
(I/O)
Use
To use more than 2 processors, the distributed and AMG solvers require ANSYS Mechanical HPC
licenses. For detailed information on the AMG solver, see Using Shared-Memory ANSYS in the
Advanced Analysis Techniques Guide. For information on the distributed solvers, see the Distributed
ANSYS Guide.
5.2. Types of Solvers
5.2.1. The Sparse Direct Solver
The sparse direct solver (including the Block Lanczos method for modal and buckling analyses) is based on
a direct elimination of equations, as opposed to iterative solvers, where the solution is obtained through an
iterative process that successively refines an initial guess to a solution that is within an acceptable tolerance
of the exact solution. Direct elimination requires the factorization of an initial very sparse linear system of
equations into a lower triangular matrix followed by forward and backward substitution using this triangular
system. The space required for the lower triangular matrix factors is typically much more than the initial
assembled sparse matrix, hence the large disk or in-core memory requirements for direct methods.
Sparse direct solvers seek to minimize the cost of factorizing the matrix as well as the size of the factor using
sophisticated equation reordering strategies. Iterative solvers do not require a matrix factorization and typically
iterate towards the solution using a series of very sparse matrix-vector multiplications along with a
preconditioning step, both of which require less memory and time per iteration than direct factorization.
However, convergence of iterative methods is not guaranteed and the number of iterations required to
reach an acceptable solution may be so large that direct methods are faster in some cases.
Because the sparse direct solver is based on direct elimination, poorly conditioned matrices do not pose
any difficulty in producing a solution (although accuracy may be compromised). Direct factorization methods
will always give an answer if the equation system is not singular. When the system is close to singular, the
solver can usually give a solution (although you will need to verify the accuracy).
The ANSYS sparse solver can run completely in memory (also known as in-core) if sufficient memory is
available. The sparse solver can also run efficiently by using a balance of memory and disk usage (also known
as out-of-core). The out-of-core mode typically requires about the same memory usage as the PCG solver
(~1 GB per million DOFs) and requires a large disk file to store the factorized matrix (~10 GB per million
DOFs). The amount of I/O required for a typical static analysis is three times the size of the matrix factorization.
Running the solver factorization in-core (completely in memory) for modal/buckling runs can save significant
amounts of wall (elapsed) time because modal/buckling analyses require several factorizations (typically 2
- 4) and repeated forward/backward substitutions (10 - 40+ block solves are typical). The same effect can
often be seen with nonlinear or transient runs which also have repeated factor/solve steps.
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5.2.1.The Sparse Direct Solver
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