HPC for industry

The blog post below is based on the abstract of a talk at the PASC mini-symposium 'Modelling Cloud Physics: Preparing for Exascale' (Zurich, 13 June 2019).

The Met Office NERC Cloud model (MONC) is an atmospheric model used throughout the weather and climate community to study clouds and turbulent flows. This is often coupled with the CASIM microphysics model, which provides the capability to investigate interactions at the millimetre scale and study the formation and development of moisture. One of the main targets of these models is the problem of fog, which is very hard to model due to the high resolution required – for context the main UK weather forecast resolves to 1km, whereas the fog problem requires 1metre or less.

EPCC is at the heart of the Data Driven Innovation programme of  the Edinburgh and South East Scotland City Region Deal. This £661m programme will lead to the creation of major new data services for Scottish business, with EPCC providing the World Class Data Infrastructure (WCDI) that will underpin it.

Last week we hosted an event to explain the enormous potential of data-driven innovation for industry and to show how companies are already using data technologies to enhance commercial performance.

The Fortissimo 2 project ended on 31 December 2018. Together with its predecessor (the plain old 'Fortissimo project') it has helped over 100 SMEs and mid-caps to run experiments that demonstrate the effectiveness of providing HPC services using a business model derived from cloud computing, thereby making it much lower risk for small companies to use HPC. 

Earlier this year, HPE announced the Catalyst UK programme: a collaboration with Arm, SUSE and three UK universities to deploy one of the largest Arm-based high performance computing (HPC) installations in the world. EPCC was chosen as the site for one of these systems; the other two are the Universities of Bristol and Leicester.

EPCC's system (called 'Fulhame' after pioneering chemist Elizabeth Fulhame) was delivered and installed in early December. This HPE Apollo 70-based system consists of 64 compute nodes with two 32-core Cavium ThunderX2 processors (ie 4096 cores in total), 128GB of memory composed of 16 DDR4 DIMMs, and Mellanox InfiniBand interconnects. It will be made available to both industry and academia, with the aim to build applications that drive economic growth and productivity as outlined in the UK government’s Industrial Strategy.

In February EPCC will host an event to explain why data driven innovation is important for industry. We will also showcase how companies are already using data technologies to enhance commercial performance.

There is a lot of hype around big data and big computing for business, but it is undeniable that the influence of data-driven innovation will be profound.

The expertise and support available in Scotland has created a massive opportunity for our engineering and manufacturing sectors and, with the launch of the £500m Data-Driven Innovation (DDI) strand of the Edinburgh and South-East Scotland City Region Deal, this is an exciting time for exploring how technology can benefit business.

2018 continues to be a busy year for EPCC in terms of industrial collaborations. Here's an overview of some of our recent partnerships.

A consortium led by Rolls-Royce and EPCC was recently awarded an EPSRC Prosperity Partnership worth £14.7m to develop the next generation of engineering simulation and modelling techniques, with the aim of developing the world’s first high-fidelity simulation of a complete gas-turbine engine during operation.

December 2018 marks the end of the Fortissimo project. EPCC has been collaborating in the programme since 2013, and during this time 92 business experiments have been carried out with over 100 small European companies, many of which had no previous experience of using HPC.

Professor Peter Boyle introduces the new DiRAC system, which is hosted at EPCC's Advanced Computing Facility. 

DiRAC is part of the UK Government’s investment into the UK’s e-infrastructure to improve computing, software development, data storage, networking and distributed computing networks.

We are working on a machine-learning project with Rock Solid Images (RSI), a geoscience consulting firm that provides borehole characterisation with the goal of reducing exploration drilling risk for oil and gas companies.

RSI is one of the main players in the interpretation of seismic data with well log data and it has built its business on using advanced rock physics methods combined with sophisticated geologic models to deliver highly reliable predictions of where oil and gas might be found.