Friday, January 27, 2017 - Amsterdam, Netherlands – Bright Computing, a global leader in cluster and cloud infrastructure automation software, today announced that it has teamed up with SGI to co-sponsor a High Performance Computing & Big Data event in London, on February 1st, 2017.
Bright Computing formed a partnership with SGI in September 2016. The following month, the two companies announced they had been selected by the UK Met Office to provide a new HPC system for weather and climate data analysis.
Bright and SGI will co-sponsor High Performance Computing & Big Data 2017, taking place at the Victoria Park Plaza hotel, on Wednesday February 1st. The event promises to showcase the latest advances in the pioneering technologies and practices which are revolutionising compute- and data-intensive research across the public and private sector. Keynote speakers include Daniel Zeichner, MP and Chair of All-Party Parliamentary Group on Data Analytics; Professor Anthony Lee, Strategic Programme Director for the Turing-Intel Programme, and Dave Underwood, Deputy Director of HPC at the Met Office.
At the event, Bright Computing and SGI will share a one-hour seminar on: Maximising Your Investment in High Performance Computing. The session will be based on the principal that HPC infrastructure represents a significant capital expenditure and depreciates over time, for example, a £3 million investment in HPC will typically depreciate at a rate of £1 million per year; that’s £2,740 per day. Dr Ben Bennett, Head of W/W HPC Marketing at SGI, and Lee Carter, VP W/W Alliances at Bright Computing, will explain how to get maximum value out of HPC hardware and improve your ROI using SGI and Bright technologies. The presentation will include a case study on how the Met Office chose a Bright / SGI solution to launch their new HPC system and significantly improve productivity of weather and climate data analysis.
To arrange a meeting with Bright at the event please email firstname.lastname@example.org
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 711315 Bright Beyond HPC