As the automotive industry continues to advance into the world of high-performance computing (HPC), it becomes increasingly crucial to achieve environmental parity for seamless software integration. In this blog post, we will explore the synergy between Elektrobit and Canonical at the core of ‘EB corbos Linux – built on Ubuntu‘ in the context of automotive computing.

With multiple technological innovations all converging at the same time, we are living in an exciting era for the automotive industry. From AI to 5G, and plenty in between, we can expect to see a host of groundbreaking trends emerge this year. As electric vehicles (EVs) completely disrupt the market and the OEMs’ business strategies, the customer focus is shifting away from traditional internal combustion engine (ICE) vehicles, challenging the way that cars are being built and designed.

Organisations are reshaping their digital strategies, and AI is at the heart of these changes, with many projects now ready to run in production. Enterprises often start these AI projects on the public cloud because of the ability to minimise the hardware burden. However, as initiatives scale, organisations often look to migrate the workloads on-prem for reasons including costs, digital sovereignty or compliance requirements.

In essence, High Performance Computing (HPC) is quite simple. Speed and scale. In practice, the concept is quite complex and hard to achieve. It is not dissimilar to what happens when you go from a regular car to a supercar or a hypercar – the challenges and problems you encounter at 100 km/h are vastly different from those at 300 km/h. A whole new set of constraints emerges.

As enterprises grapple with the evolving landscape of security threats, the need to safeguard internal networks from the broader internet is increasingly important. In environments with restricted internet access, it can be difficult to manage software updates in an easy, reliable way. When managing devices in the field, change management and compliance policies can introduce even more complexity to the update process. You can solve these challenges using snaps and the Snap Store Proxy.

We’ve all heard about cloud-native applications in recent years, but what about cloud-native infrastructure? Is there any reason why the infrastructure couldn’t be cloud-native, too? Or maybe it’s already cloud-native, but you’ve never had a chance to dive deep into the stack to check it out? What does the term “cloud-native infrastructure” actually even mean? The more you think about it, the more confusing it gets.

In the ever-evolving landscape of cloud computing, the concept of a sovereign cloud has recently emerged in response to data management challenges. As governments increasingly recognise the importance of safeguarding their data, ensuring compliance with local regulations, and asserting digital autonomy, sovereign cloud solutions have gained prominence. This blog will explore this concept in detail.

The term High Performance Computing, HPC, evokes a lot of powerful emotions whenever mentioned. Even people who do not necessarily have vocational knowledge of hardware and software will have an inkling of understanding of what it’s all about; HPC solves problems at speed and scale that cannot be achieved with standard, traditional compute resources. But the speed and the scale introduce a range of problems of their own.

As we’ve seen throughout this series, a design pattern is a general solution that has been proven to solve a repeatedly occurring problem when designing software. In my previous blog posts, we examined the basics of the software operator design pattern, the forces impacting it, and the advantages and disadvantages of using it. But how does the software operator pattern actually work?

2023 was an epic year for artificial intelligence. In a year when industry raced faster than academia in machine learning (source), the state of the art for AI evolved to include increasingly larger amounts of data, and bringing to bear sufficient computing resources to support new use cases remained a challenge for many organisations. With the rise of AI, concerns were not far behind.