Mechanical APDL Basic Analysis Guide

Disk

(I/O)

Use

Ideal Model Memory Use

Size

Solver Typical Applications

chine, 1.0

GB/MDOF

10

GB/MDOF

on slave

machines In-

core:

In-core:

15

1

GB/MDOF

GB/MDOF

on mas-

ter ma-

chine, 10

GB/MDOF

on slave

machines

0.5

GB/MDOF

1.5-2.0

GB/MDOF in

total*

1 MDOF to

100 MDOF

Same as PCG solver but can also be

run on distributed memory parallel

hardware systems.

Distributed

Memory

PCG Solver

0.5

GB/MDOF

0.5 GB/MDOF

in total*

1 MDOF to

100 MDOF

Same as JCG solver but can also be

run on distributed memory parallel

Distributed

Memory JCG

Solver hardware systems. Not as robust as

the distributed memory PCG or

shared memory PCG solver.

• In total means the sum of all processors.

Note

To use more than 2 processors, the shared memory and distributed memory solvers require

HPC licenses. For information, see HPC Licensing in the Parallel Processing 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 ac-
ceptable 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 sophisticat ed 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

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Types of Solvers