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This technical report introduces the scope of JMAG's technological development.
For this first issue, we raise the question of why a high-speed calculation
engine is necessary and address the various ways a matrix solver has been
integrated into JMAG.
CAE's role has vastly changed in the last 10 years following the expansion
of CAE. Three dimensional analysis is becoming more common which has lead
to an increasing number of elements that need to be evaluated (see Fig.
1). For this reason, high speed capabilities of the calculation engine
have become an essential part of the technology.
Fig. 1. Increasing Number of Elements
As previously mentioned, the needs of engineers utilizing CAE tools has
diversified. At the very least, CAE tools support the analysis operations
of users whom demand a program which can easily and efficiently evaluate
a variety of calculations. Furthermore, as each day passes, more and more
users require the ability to analyze phenomena in greater detail. To provide
software to meet these requirements functionality is important, but developing
a high-speed calculation engine is vital.
Usability
As users around CAE have most likely noticed, JMAG provides a variety of
functions. One example is the ability to use CAD models designed in CAD
systems for analyses. A precise CAD geometry model created using a CAD
system can be used easily via the functions to link JMAG to a CAD system.
However, the number of elements required increases dramatically to accurately
evaluate a model with complicated geometry.
This is why JMAG provides an automatic mesh generation function that allows
users with little to no experience generating mesh the ability to generate
mesh appropriate for an analysis. A high quality mesh can be generated
based on the modelfs geometry and internal design, such as the gap. However,
because generating only the minimum number of elements necessary with the
auto mesh function is difficult, an experienced user that can control how
the mesh is generated manually is advantageous.
Calculating a Variety of Parameters Efficiently
"I want to increase reliability of my designs by investigating a number
of parameters." Many designers desire the same ability. JMAG provides
a parametric calculation function that allows users a method to automatically
calculate a large number of design possibilities efficiently.
Analyzing Designs in Greater Detail
Invisible phenomena, such as magnetic flux density distribution, can be
analyzed because JMAG is a computer aided engineering tool. Naturally,
designers want to evaluate the physical phenomena inside their designs
accurately. JMAG not only provides a wide range of modeling methods for
geometry, but also simulates physical phenomena that include material properties,
heat, and the structural makeup of a design.
Innovations to handle a large number of elements and calculation parameters
as well as increasing the scale of the analyses are necessary to fulfill
the diverse needs of our users. This is what drives us as we continue to
develop a high-speed calculation engine.
Quintupling the Speed Yearly
JMAG has been developed to provide a tool that can drastically reduce costs
and increase efficiency throughout the design process via high-speed analyses
for a variety of different analysis targets.
JMAG utilizes a wide range of ingenuity to achieve the high-speed analyses
that it offers. The main innovations focus on improving the algorithms
and calculation processes as well as implementing a function for parallel
computing. The amount of time required for an analysis in version 9.1 has
been reduced to 1/5 the amount of time required in version 8.3, as indicated
in Fig. 2. Although the time required depends on the type of analysis,
JMAG has successfully quintupled the analyses over 4 years.
Fig. 2. Time Require for an Analysis in JMAG by Version [1]
If It's Not Fast, It's Not Worth Using
Why is the speed of JMAG such a large part of the development process?
Speed is vital to meet the demands designers face each day.
The number of designers using CAE tools is increasing as the performance
analysis software offers grows. In an extremely competitive product development
industry there is constant pressure to reduce the number of prototypes
as well as the amount of time for developing a product. Research has shown
that the amount of time allotted for design in the automotive industry
has decreased by 40% in the last 10 years.
JMAG is implement into the design process to increase efficiency and reduce
costs. The speed of the solver is indispensable to meet these demands.
Therefore, if the solver is not fast, it is not worth using.
Parallel Computing for the ICCG Calculation Method
JMAG has implemented parallel computing to attain a faster analysis. Why
parallel computing? Parallel computing is used because the matrix solver
for linear equations requires the most time for an analysis. Analyses performed
using ICCG make up 80% to 90% of the time required for calculations performed
based on our internal research. Therefore, JMAG has been designed to perform
analyses with ICCG by implementing symmetric multiprocessing (SMP). The
speed attained using symmetric multiprocessing is indicated in Fig. 3.
Compared to an analysis that uses a single processor, an analysis that
is distributed over eight processors offers 3 to 4 times the speed. Furthermore,
these results were obtained using computers with two Intel® Xeon 5500
Quad Core CPUs.
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Fig. 3. Speed Achieved with Parallel Computing
(left : analysis model, right : comparative speed)
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Robust Parallel Solver
The robust parallel solver JMAG offers is vital for both the hardware and
the solver. How was this robust solver realized? Let's take a look at three
factors that influence the development of the solver:
(1) Optimum Performance on Various Hardware
Unfortunately, the performance of parallel computing differs according
to the hardware that is used. How can a high level of performance be accomplished
on an assortment of hardware? A vast amount of trial and error with a lot
of fine tuning is required.
The parallel computing function implemented in JMAG was tested on a wide
variety of the latest hardware with the cooperation of the hardware vendors.
A lecture the Sumisho Computer Systems Corporation presented at the JMAG
Users Conference 2008 regarding the present and future state of multi-core
processors used the ICCG solver in JMAG as their example.
The hardware JSOL used to test and refine the solver in JMAG is indicated
in Fig. 4. Seven out of nine of the CPUs that could effect parallel computing
had already been tested with JMAG.
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Fig. 4.Hardware Tested for Parallel Calculation
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(2) Focusing on Solver Stability
Regardless of how fast an analysis can be performed using parallel computing,
the analysis is useless without the ability to obtain accurate results.
This is why the primary focus for the development of JMAG is stability.
Various measures have been researched and tested to achieve a materix solver
in JMAG that provides the highest level of convergence.
A wide range of tests have been performed to successfully develop a high-speed
solver that uses parallel computing while maintaining stability.JMAG provides
a faster solver for a variety of analyses from static to transient and
frequency response analyses that can be performed for a wide range of analysis
targets that include motors, magnetic heads, busbars, and magnets.
(3) Applying Algorithms Accounting for the Number of Threads
The algorithm used for parallel computing depends on the number of threads
(number of cores) used. For example, the performance of an algorithm that
offers a vast amount of speed for a small number of threads decreases when
the number of threads exceeds a certain point. For this reason, it is necessary
to select an algorithm based on the environment that will be used.
Presently, the maximum number of threads supported for multi-cores is 8.
Therefore, JMAG applies algorithms specifically for this size of parallel
computing.
Parallel Computing and Hardware Innovation
Technology for parallel computing is influenced by innovations in multi-core
hardware. Recently, hardware that applies parallel computing has been introduced
to the market as manufacturers such as Intel® and AMD continue to revolutionize
multi-core computer processors. Software developers are currently focused
more on utilizing multi-core parallel computing because increases in the
clock speed of the CPU environment can no longer be expected.
Increasing the Speed of JMAG
In the future, innovations for multi-core computer processors will continue
to be a vital part of implementing further innovation into JMAG.
This innovation is not only being applied to further development of a parallel
solver, but also to increase the speed of analyses performed on a single
processor. CAE tools do not make the products. However, JMAG aims to provide
the fastest magnetic field analysis solver to assist our users in designing
the highest quality of products possible.
The next edition will introduce how the mesh generation engine has been
enhanced to meet the diverse needs our users have.
[1] : From the JMAG Users Conference 2008 Poster Session
[2] : From our internal research
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1. Implementing JMAG 
