Parallel version solver
based on iterative solution method

Super
Matrix Solver P-ICCG

Special features
of Super Matrix Solver P-ICCG

- Proven performance applicable to
diverse fields of analysis such as electromagnetic and structural
analyses.
- Wide range of usage from 1 CPU, 2CPU's to full-scale parallel processing.
- Readily installed with easy-to-understand product manuals.
- No need for special mathematical knowledge-easy integration of modules
provided in executable format (such as DLL).
- Supports various types of platforms.
- Complete with support system for evaluation and use.

What is ICCG
Method?

- ICCG is an iterative solution method
based on CG (Conjugate Gradient) method. In ICCG, the calculation
speed of CG method is enhanced with pre-processing technology (Incomplete
Cholesky Factorization). Compared with CG method that has no pre-processing,
ICCG method is faster and more stable.
- ICCG is an iterative method with many actual performance results in diverse
analysis fields such as structural, electromagnetic and computational fluid
dynamic analyses.

P-ICCG Summary
Specifications

Items

Descriptions

Notes

Target Analysis Fields

Structural Analyses and Electromagnetic, etc.

Target Coefficient Matrix

Sparse matrices that are generated from discretization methods
such as finite element, finite volume and differential methods.

Problems with Zero Diagonal Elements

Capable of calculating.

Not all problems with zero diagonal elements
can be calculated.

Types of Unknowns

Real (double) and Complex Numbers

Symmetry of Problems

Symmetric problems only. Unable to calculate asymmetric problems.

Parallelization Method

Supports shared memory type (SMP)

Maximum Number of CPU's

Technically unlimited; however, 1 to 8 CPU's are recommended.

Users must purchase the same number of licenses
as the number of CPU's used.

Input Data

Coefficient matrix, right-hand side vector, target convergence,
maximum number of iteration, etc.

The number of CPU's to be used in calculation
may be assigned.

Output Data

Solution vector, achieved relative residual, actual number
of iteration, etc.

Indication of Error Messages

Warnings and error messages returned as return value (calculation
information, system information, etc).

Method of Provision

DLL format for Windows; Static library format for Linux and
UNIX.

Source code will not be disclosed.

Attached Materials

Product manual (with explanations about data format, parameters,
integration procedures, etc.), sample data, sample program
for integrating SMS-AMG (C and FORTRAN).

P-ICCG's Parallel
Calculation Performance

Actual Calculation Time (sec. )

Types
of Problems

Number
of Unknowns

Convergence
Time (sec.); target convergence: norm＜1.0e-10 )

1-CPU

2-CPU

4-CPU

8-CPU

Magnetic field analysis

220K

87.0

43.6

26.7

13.4

Fluid analysis

500K

78.3

54.2

32.7

19.3

Structural analysis

250K

1227.5

744.7

390.4

224.1

Fluid analysis

1000K

178.5

141.7

82.7

48.5

Comparison of Calculation Performance
(1 CPU calculation speed as 1)

Types of
Problems

Number of
Unknowns

Convergence
Time (sec.); target convergence: norm＜1.0e-10 )

NOTE: Materials provided on this web site does not guarantee functions
and performance of the product.
Product specifications may change without notice.