Nick is Senior Director at Verne Global and leads our work across HPC and specifically its implementation within discrete and process manufacturing. He is based in our London headquarters.
As I speak to customers, I am struck by a profound misconception about the banking and financial services industry. Traditional as they may be, technology laggards they are not. Innovative companies are creating and processing data at industrial scale. They are deploying machine learning for everything from risk assessment models to personalised user experiences to fraud detection. The need to analyse these vast and complex data sets is resulting in applications that are both data- and power-hungry.
As with all tradeshow events this year, ISC High Performance 2020 took on a very different look and feel. Overall, the event received high praise for adapting to a virtual environment, and the news coinciding with the event continued to generate headlines. One of the more interesting announcements was from NVIDIA and Mercedes Benz launching software-defined, intelligent vehicles using end-to-end NVIDIA technology.
There has been recent chatter in the automotive industry news about high-performance computing (HPC) as it relates to speed, price and automotive applications. I’d like to break some of this down a bit further and explore why automotive is the next great area for HPC.
It’s been a while since I wrote a blog so I thought there was no better opportunity to pick up where I left off and write something about my recent trip to the Meteorological Technology World Expo in Amsterdam. This was also the venue for where we announced our latest customer win – Centro Epson Meteo, one of Europe’s most innovative meteorological forecasting organisations who I am delighted to say chose Verne Global for their high performance computing (HPC) requirement.
Building accurate road maps is a central part of the effort to build and deploy more autonomous vehicles in the real world. The term “map” may be a bit of a misnomer, though, because these maps aren’t anything like the flat 2D images available online, they’re complete three-dimensional recreations of roadside environments that are updated on a continuous basis to provide a high degree of accuracy — often down to the centimeter scale. These 3D digital maps are a critical part of an autonomous vehicle’s ability to perceive the world, and have key applications in other technologies, which has made the effort to develop the definitive map a highly competitive endeavour.
An ‘oil gusher’, or a 'blowout', is the name for that phenomenon that you’ve seen in photos and film clips, when a drill strikes oil and it sprays out of the top of the well. It was common in the early 20th Century but is now quite rare, thanks to pressure control equipment. However in today’s oil and gas industry, data is the modern gusher – it sprays out in an uncontrolled fashion, signifying that something good is going on but it remains hard to get under control.
Since Sebastian Thrum and his team used machine learning to win the DARPA Grand Challenge in 2005, machine learning and deep learning have been an integral part of developing autonomous vehicle technology. Great progress is being made, but complex questions remain. My latest blog looks at these issues.
In my previous blogs I've highlighted how high performance computing (HPC) has become a powerful tool aiding automobile design. HPC has been particularly important in the realm of simulated crash test simulation and this blog focuses on the rapid improvements being made in this field.