Advanced Computing for Fluid Solver
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15-16 September 2016
AMAZON, Web Services, London, UK
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Coordinator: Dr.  David Standingford, Zenotech, UK
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Open for registration: richard.seoud-ieo@ercoftac.org
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Course summary:
Many experienced flow, turbulence and combustion (FTAC)
practitioners and new researchers are aware of the rapid
rates of progress in many areas of information and
communication technology (ICT).  The opportunities that this
new power provides include (i) accelerated processing speed,
(ii) large data set storage and interaction, (iii) remote
manipulation and visualisation of full data sets, and (iv)
reduction in the cost of simulations. The benefits are often
perceived to be hard to access because of the domain
specific knowledge required to employ new methods.  In this
course we will provide guidance on and hands-on training in
the use of openly available tools.  Note that attendees
should bring a laptop for the tutorial sessions.
Background:

Prior to 2000, most conventional computers used single-core
processors (CPUs). Year-on-year performance improvement in
turnaround time for computational fluid dynamics (CFD) was
achieved with increasing clock speeds – until we simply
could not make the chips run any faster.  Within the IT
community, there is an expectation that computing
performance will follow “Moore’s Law” – an observation about
the trend for increasing power via transistor density in
early computer chips.  This “Law” has seen computing power
increase exponentially – roughly doubling every 2 years.
With the limit on clock speed, the industry started
increasing the number of CPU “cores” in each chip in order
to keep delivering more power.  This age of “multi-core”
chip architecture offers both opportunity and challenges for
authors of fluid dynamics solvers.  An extreme example of
multi-core hardware is the modern graphical processing unit,
or GPU.  These were initially developed for the gaming
industry, and rapidly found an application in high
performance computing (HPC). GPU’s have thousands of cores
and run at a lower clock speed than CPUs – thereby
delivering enormous power at very high efficiency.  A single
modern GPU can deliver more than a Teraflop of double
precision floating point performance.  This is equivalent to
approximately 20 CPUs and the price performance can be very
significant.  The largest GPU-based computers worldwide -
for example TITAN at Oak Ridge National Labs, with 18,000
GPUs can deliver of the order of 10 Petaflops in double
precision.  However, accessing this power requires software
to be written in a specific way.  It is generally not a
trivial task to port existing code to run on GPUs, though
some benefit can usually be derived by off-loading linear
algebra from the CPU to the GPU.   For developers willing to
re-structure their code to take full advantage of GPUs, the
performance on CPUs often increases as a by-product! In the
course we will provide an introduction to GPU technology and
a hands-on tutorial for participants to start developing
their own GPU-based software.

Cloud computing is now everywhere – we use it to watch
streaming movies, exchange files and post pictures on social
media sites.  The hardware required to deliver this new
digital world is massive and has created a new market in
on-demand computing resource.  For users of HPC, it is no
longer necessary to purchase and manage large computers –
rather they can access the necessary hardware over an
Internet connection.  This can be done in a secure and
private manner, and at a fraction of the cost.  The
opportunity to transfer capital expenditure (CAPEX) to
operational expenditure (OPEX) – as and when needed – is
highly attractive to company finance directors, especially
in times of economic austerity. Market leader AMAZON made an
early start by moving its own operations to a
service-oriented architecture, but others have followed
suit.  In the course we will show how a range of cloud-based
solutions can deliver computing on-demand.  Course
participants will be able to explore a range of cloud
computing capabilities in a hands-on session, and are
invited to make suggestions for software that we can
pre-install.   

The opportunity to use on-demand hardware brings with it the
question of data transfer.  The input files to set up and
run a fluid dynamics simulation are usually manageable in
size, but the output files can be enormous.  An alternative
to data download is remote visualisation and
post-processing. This allows the user to keep the data in
the cloud, and interact with it to produce the graphs, plots
and outputs (even videos) that they need. In the course we
will show alternatives using the open-source ParaView
toolkit from KitWare.

The new technology options and hardware suppliers has
created a new industry in customisation and solution
delivery.  Smaller companies can now be entirely cloud-based
and offer integrated fluid dynamics capability by combining
the on-demand hardware with specific software products.  In
the course we will explain how Zenotech and NICE are
exploiting the current technology trends and participants
will be able to talk directly with the “middle-ware”
developers to understand the details of on-demand computing.
 In particular we will address concerns regarding data
security and strategies for data management – often
perceived as barriers to using these low-cost highly
powerful systems.

This course is intended to be a gentle introduction to these
new technology trends, with hands-on participation.  The
course is designed for non-IT specialists (academic and
industrial) with an interest in more powerful, flexible and
cost effective fluid dynamics simulation options.
Outcomes of the course are:

1. Understanding and using GPU-based computing for powerful
fluid dynamics simulation.

2. Hands-on use of cloud-computing technology for on-demand
resource.  

3. Understanding remote data access and visualisation

4. Networking with market leaders and gaining insights into
future trends. 
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http://www.ercoftac.org/events/advancedcomputing_for_fluid_solver/
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Fees:
Members, €595, Non-Members, €895
PhD Student Members: €450
PhD Student Non-members: €550