Getting Started with Hybrid Cloud

By Bill Harries | June 30, 2020

In my last couple of posts I’ve talked about High Performance Computing in the cloud. Specifically about extending your compute clusters to take advantage of additional compute resources when needed without having to invest in all the hardware, software, etc to take care of short term demand for additional capacity.

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Getting Started with Easy8

By Robert Stober | June 25, 2020

When we launched the Bright “Easy8” program and our BEACON user community in November last year, we took the unprecedented step of making Bright Cluster Manager free for clusters up to 8 nodes. This empowered organizations to reap the benefits of our mature commercial cluster management system at no charge.

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"A New Era in HPC" – Bright to Present at Dell’s Virtual HPC Community Meeting

By Grant Gustafson | June 23, 2020

For many years, Bright has been a regular participant and contributor to Dell’s HPC Community Meetings, so I was saddened that these physical events – like all other HPC industry events – have been put on hold for the foreseeable future. They present such a great networking opportunity, enabling customers and partners to connect and giving us all a platform to share our vision for the HPC industry. With so many of us mostly home-based, I think we are all missing face-to-face contact with our HPC peers.

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Multiple Workload Management Systems - And Bright

By Martijn de Vries | June 17, 2020

Bright Cluster Manager has always integrated with workload management systems. And, system administrators control many aspects of their workload management system from Bright Cluster Manager’s management interface, Bright View. 

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SDSC Leverages Bright Cluster Manager in new ‘Expanse’ Supercomputer

By Bright Staff | May 27, 2020

We recently announced that the San Diego Supercomputer Center (SDSC) will be using Bright Cluster Manager to manage the facility’s newest supercomputer, called ‘Expanse’. Bright Cluster Manager will enable Expanse to balance and manage resource diversity across virtually all domains of their science and engineering users, maximizing resource utilization, and increasing workload efficiency for research scientists across the country and beyond.

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Goodbye to an Industry Icon

By Jill King | May 26, 2020

These words are being uttered around the globe today as the announcement went out that Rich Brueckner from insideHPC has passed away. Many called him a friend, including myself. An industry icon has gone, and we will mourn the loss for some time to come. Today, I reflect on the man and the icon.

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Bright Cluster Manager 9.0 - Job Data, Jobs, and Project Managers

By Martijn de Vries | May 21, 2020

In recent weeks, I’ve been blogging about some of the cool new features in Bright Cluster Manager 9.0, to share an insight into the depth and breadth of the latest version of our technology.

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Exciting New cmjob Features in Bright Cluster Manager 9.0

By Martijn de Vries | May 14, 2020

Typically, when an organization extends an on-premises cluster into Amazon Web Services (AWS) or Microsoft Azure, there isn’t the same storage mounted on-premises as there is in the cloud. If you have a very fast connection and if you would mount your on-premises storage on your cloud nodes, there wouldn’t be a problem, as all data would be available both on-premises and in the cloud. However, if you don't have a fast link, the input data for a job would need to be moved to the cloud, and the output data would eventually need to come back to the on-prem cluster. 

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Using Bright Cluster Manager to leverage Cloud Resources

By Bill Harries | May 07, 2020

I just finished reading an article on The Next Platform – “The Outlook for Infrastructure is Cloudy – in a Good Way”, and feel compelled to comment, especially given my previous post on High Performance Clusters in the Cloud?

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Navigating Virtual Data Centers for Autonomous Driving - Don’t Miss a Great Webinar

By Grant Gustafson | April 28, 2020

The development of advanced driver assistance systems (ADAS) - also known as autonomous driving (AD) - relies heavily on massive amounts of real-world training data that consists of data gathered over the course of millions of miles of test driving and thousands of concurrent simulations. To be successful, car manufacturers must be able to simultaneously ingest thousands of concurrent streams of data, apply artificial intelligence (AI) techniques to Petabyte size training data sets, and archive these datasets for decades to come.

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