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Hardware and Software Requirements

Platforms

WIPL-D software is supported on:

CPU Operating system Application
Intel Pentium, Core, Core2 (32 bits) / AMD CPUs (32 bits) Win 2000, Me, NT, XP, Vista WIPL-D Pro, WIPL-D Microwave, WIPL-D Optimizer, AW Modeler
Intel EM64T CPUs - Pentium, Core, Core2, Xeon (64 bits) Win XP x64, Vista WIPL-D Pro, WIPL-D Microwave, WIPL-D Optimizer, AW Modeler
Intel Itanium 2 (64 bits) Windows 2003 Server WIPL-D Pro
Intel Pentium (32 bits) Linux Red Hat WIPL-D Pro kernel (GUI not supported)
AMD Athlon, AMD Opteron, ... (64 bits) Linux SuSE 9.0 and above WIPL-D Pro kernel (GUI not supported)

Cluster version of WIPL-D Pro is also available. The performance was measured on a cluster consisting of Intel Pentium D CPUs, running Red Hat Enterprise Linux, where it achieved around 0.75 parallel efficiency. This means that on a 8-node cluster, the software ran 6 times faster than it would on a single node.

 

Memory Requirements

 

On 64 bit systems, it is possible to use more than 2 GB of RAM for calculations, thus it is possible to solve problems with more than 15000 unknowns in-core.

RAM requirements consist of a fixed amount which is about 400 MB, and an additional amount which, for various numbers of unknowns, is given in the table below. Recommended minimum for the professional edition of WIPL-D Pro is 1 GB RAM and 200 MB hard disk space.

Number of unknowns RAM [MB]

(single precision)

RAM [MB]

(double precision)

1000 8 16
3000 72 144
5000 200 400
7000 392 784
9000 648 1296
11000 968 1936
13000 1352 2704
15000 1800 3600
20000 3200 6400
30000 7200 14400
60000 28800 57600
100000 80000 160000

*RAM [Mb] = 8*N*N (in thousands) for single precision. For double precision the requirements are doubled.

Order of Magnitude Ahead

New!  WIPL-D Pro 8.0
3D Electromagnetic Solver

Simulation of electrically large structures
Interactive circuit and 3D EM co-simulation
Analysis of arbitrary 3D structures
Easy modeling with a powerful GUI
Fast and accurate, sophisticated numerical engine
Variety of output results
Hybrid and multiminima optimization algorithms
Import of models from AutoCAD
Great acceleration using GPU Solver
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