Energy supply industry design capability and chip manufacturers’ market performance are significantly enhanced by integrated computer hardware and software
Submitting InstitutionUniversity of Bedfordshire
Unit of AssessmentComputer Science and Informatics
Summary Impact TypeTechnological
Research Subject Area(s)
Information and Computing Sciences: Artificial Intelligence and Image Processing, Computation Theory and Mathematics, Computer Software
Summary of the impact
Effective industrial design and simulation require efficient and
versatile computing systems. As a result of research performed by our team
experienced in High Performance Computing (HPC), novel software structures
and aligned hardware architectures have led to significant benefits to the
energy supply industry and to microprocessor manufacturers.
As a result of our research with supercomputing, simulation times for
electric field patterns in power components have reduced more than
30-fold, with accurate complex 3-D outputs for an increased range of
configurations, thereby enabling our partner company to achieve results
not possible with commercial software and to reduce product development
costs by $0.5M - $5M p.a.
Our research has been incorporated by Intel into their numerical
libraries and now made available to the general public supported by their
latest processor architectures. Intel now has a 82% share of processors,
according to the November 2013 Top500 list.
Prof. Carsten Trinitis, Professor of Distributed Computing (since 2010)
Dr Mehmet Aydin, Lecturer in Computer Science
In the research described, Prof. Trinitis also collaborated with staff at
the Technical University of Munich, Germany, in particular Dr Alexander
Heinecke, Dipl-Math Thomas Müller, Dr Josef Weidendorfer and Dr Benoit
Since the early 1990s, researchers led by Prof Carsten Trinitis have been
engaged in pioneering fundamental research to develop and apply high
performance computing techniques to both the global energy supply and
medical fields, while leading microprocessor vendors have recently
incorporated the results of the research group's algorithms and designs
into numerical libraries on their latest processors.
In the design process for high voltage electrical components, Asea Brown
Boveri (ABB) needed to simulate the field patterns for transformers and
switchgear from the early 1990s, but the tools available to them at the
start of their association with Prof. Trinitis' High Performance Computing
group could only provide simulations for simple two-dimensional or
axi-symmetric geometries. There was a strong demand for accurate, fast,
three- dimensional component simulation and optimisation to reduce the
overall design time and to enable earlier, lower-risk commencement of
manufacture. According to ABB's corporate research centre brochure,
"Experimental trials, theoretical modelling, and numerical simulations are
the tools for success".
In 2010 Prof. Trinitis became Professor of Distributed Computing at the
University of Bedfordshire (UoB). The work summarised below is built on
work which began at the Technical University of Munich (TUM), Germany, and
carried out at both TUM and UoB since 2010. Optimisation algorithms
developed by Dr Mehmet Aydin [3.1] of UoB were successfully applied to and
integrated into ABB's simulation system Toolbox and are now in
REF period activity
The research addressed and continues to address the following questions.
- Can the code be parallelised, to reduce simulation times
- Can the code utilise the underlying hardware to reduce run time and
provide a good match to the desired output format?
- Can the underlying hardware architecture be made upgradable to take
advantage of future developments in supercomputers?
- Can the user interface be made user-friendly and suitable for use by
- Can the computing process be readily upgraded as improved algorithms
All these questions have been answered positively, through the following
a) As optimisation requires multiple simulation runs, novel parallel
optimisation methods based on genetic algorithms were developed and
applied [3.1], [3.3].
b) The code was specially redesigned to take advantage of novel hardware
features at all levels: vector registers, shared memory thread parallelism
and distributed memory parallelism [3.3], [3.4].
c) Ideas from upcoming hardware architectures such as 512-bit vector
registers and many-core thread parallelism were integrated into the
research and design process [3.2], [3.5].
d) The new features were integrated into Toolbox, which is a
standard interface used by ABB engineers for carrying out simulations and
optimisation runs. These features have been successfully deployed at
various ABB research centres across Europe.
Throughout this work, close contact was maintained with engineers and
researchers at the ABB research group in Switzerland and Sweden who
trialled and deployed the research results to accelerate their simulations
of electrical power grids using a multicore architecture [3.4].
References to the research
3.1 Coordinating metaheuristic agents with swarm intelligence
Mehmet E. Aydin
Journal of Intelligent Manufacturing, 23 (4), 991-999, 2012.
This article describes the particle swarm optimisation (PSO) algorithm
that has become part of ABB's simulation system Toolbox.
3.2 Porting Existing Cache-oblivious Linear Algebra HPC Modules to
Alexander Heinecke, Carsten Trinitis, Josef Weidendorfer
In: Proceedings of the 7th ACM International Conference on Computing
frontiers, pp. 91-92, ISBN:978-1-4503-0044-5, Bertinoro, Italy, May 17-19,
International ACM conference, acceptance rate 39%
This is the first non-Intel paper which described how to write code for
Larrabee (now renamed the Xeon Phi architecture).
3.3 A Fast Kriging-based Strategy for the Optimization of Electrical
Benoit Chaigne, Carsten Trinitis
PAMM Special Issue: 82nd Annual Meeting of the International
Association of Applied Mathematics and Mechanics (GAMM), Graz 2011;
Editors: G. Brenn, G.A. Holzapfel, M. Schanz and O. Steinbach, Volume 11,
Issue 1, pages 871-872, December 2011
This paper describes how novel optimisation algorithms have been
developed and applied to ABB's field simulation environment.
3.4 Sparse Matrix Operations on Several Multi-core Architectures
Carsten Trinitis, Tilman Küstner, Josef Weidendorfer, Jasmin Smajic
The Journal of Supercomputing
Volume 57, Number 2, 2011, 132-140, DOI: 10.1007/s11227-010-0428-9
A journal article on how ABB's contingency analysis was redesigned and
optimised for several state-of-the-art multi-core processor
Details of the impact
Our research has provided economic and technical impact in both the
energy supply industry and processor manufacturers, helping the former to
carry out simulations their competitors are not capable of, saving
millions of dollars per year through this, and the latter to maintain
their position as worldwide market leader in x86 architectures [5.1, 5.2,
A Energy Supply industry
The contributions from our research group made it possible for ABB
engineers to simulate and optimise entire devices for the first time in
their company's history, saving a significant amount of money for
development and components, and putting ABB at the leading edge of energy
supply technology worldwide. Our cooperation with the group of the Senior
Scientist at ABB, Switzerland, has been very successful over many years.
It has significantly contributed to establishing high performance
computing technology as one of the basic elements in the ABB research and
development infrastructure and has had a considerable impact on many
development projects involving simulation tools.
In the 1990s our cooperation efforts were focused on speeding up the ABB
in-house codes for electromagnetic simulations as well as on creating an
efficient computing environment. In 1999, the first Linux cluster was
implemented at ABB together with Carsten Trinitis' research group. This
was a long time before cluster technology became an industry standard.
Today, several clusters are operated in ABB and the engineers at worldwide
distributed ABB sites can run the parallel codes which are continually
being optimised and adapted to latest high performance computing
technology by Carsten Trinitis' team, in their on-going relationship with
During almost 2 decades of collaboration between the modelling and
simulation team at ABB's corporate research centre led by their senior
scientist Dr Andreas Blaszczyk and Carsten Trinitis' team, ABB has
performed several projects that have been successfully been implemented as
production tools and put into production use from 2010 on. The most
important examples, beyond the parallel cluster environment mentioned
above, are as follows.
- Creation of a parametric optimisation framework used by ABB
engineers to run complex simulations in an automatic loop involving
direct coupling between CAD systems and electromagnetic simulations.
- Performance optimisation of a SPICE-based solver for fast
computation of transformer cooling; the new solver is used as a
component of an interactive transformer design system in ABB.
- Acceleration of contingency analysis of electric power grids;
increasing the efficiency of the in-house code developed by ABB Sweden
running on the new multi/many core processor architectures.
According to the Senior Scientist of ABB, Switzerland [5.1], the group of
Carsten Trinitis has shown not only a high level of professional skills in
the area of high performance computing, but they have been adjusting and
extending their own scientific goals according to the needs of industrial
cooperation. This is something which ABB considers to be very positive
(although not very common in the academic community), and which enables
new research topics to be found that are challenging to the academics and
which have practical interest and application for the industry partner.
New features which were not available before have been integrated into
their simulation system Toolbox, enabling them for the first time
to optimise day-to-day problems by reducing runtimes from 20 seconds to
200-300 milliseconds per iteration at around 1000 iterations per
optimisation run. These features are not available in commercial software,
making the flexibility ABB gained through collaboration with Carsten
Trinitis' team unique in the energy supply industry. According to their
Senior Scientist, production costs were reduced by between $0.5M to $5M
per year through this [5.1], and he plans for the collaboration to
continue over the next few years at least.
B Processor manufacturers
The work done by Carsten Trinitis' team on many-core architectures has
focussed on power efficiency, efficient algorithm implementation, and
sparse and dense matrix solvers, and has significantly helped Intel prove
and improve their many-core products. In 2009 and 2010, Intel had to make
decisions on whether a many-core architecture was actually necessary.
There were real questions if such an architecture would provide
substantial benefits to Intel's user community. Intel selected very few
sites to conduct studies on the performance and utility of their many-core
prototypes. Work done by Carsten Trinitis' team on TiffaMMy was one of the
first independent studies that validated what became Intel's MIC
Architecture. The findings of performance and programmability provided a
valuable early proof-of-concept that supported their ideas, and it helped
Intel make the choice to press forward with further investments. The
result was the Intel Xeon Phi coprocessor. Other feedback given through
Intel's software development phase and during the first product
development influenced the decision which Intel made in future Knights
Landing — the first highly parallel host CPU. The work with Carsten
Trinitis' team has been highly influential in Intel's product design,
resulting in a 82% market share in the November 2013 Top500 list [5.2],
[5.3], [5.4]. Through this share, Intel have significantly strengthened
their position in high performance computing, making them the number one
manufacturer of processors for supercomputing systems worldwide not only
in terms of compute performance, but also in terms of energy efficiency
The experience gained through the above mentioned research allowed
Carsten Trinitis' research group to participate in the development of
numerical libraries for the latest processor architectures at Intel, who
have now incorporated parts of our code into their Math Kernel Library
(MKL) for the Xeon Phi architecture [3.2], [3.5].
Sources to corroborate the impact
5.1 Senior Scientist, ABB Corporate Research Centre, Switzerland
5.2 Director of Marketing for Technical Computing, Intel Corporation, USA
5.3 Director Parallel Computing Lab, Intel Corporation, USA
5.4 Director of Leibniz Computing Centre, Garching bei München, Germany