Super Matrix Solver the high-speed and robust matrix solver
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  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 )
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 )
Magnetic field analysis 220K 1.0 2.0 3.3 6.5
Fluid analysis 500K 1.0 1.4 2.4 4.1
Structural analysis 250K 1.0 1.6 3.1 5.5
Fluid analysis 1000K 1.0 1.3 2.2 3.7
More Information
- Introduction of ICCG Parallel (SMP) Version SMS P-ICCG (PDF/558KB)
Platforms (OS)

Windows, Linux; for detailed information: P-ICCG System Environment (PDF/13KB)

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