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Recently I was part of a sales call with a prospect when he commented that a server manufacturer was proposing to include their own cluster manager product in the system they were selling. The prospect asked us, “Do you know why they want to include their own cluster software?  Vendor lock-in, that’s why!” he said.  I was a little startled by his directness.  But it is true, and it feels a bit like a dirty little secret that some providers keep from their customers.  Being locked into a particular vendor carries a great deal of emotion (especially in the high-tech sector) because organizations work very hard to ensure they have choice and flexibility in their business.  But what does this have to do with Bright Computing?  My answer is plenty!

The Virtual Bright User Group meets twice a year to hear the latest product roadmap from Bright and to provide feedback into our strategy and product direction.

We are rapidly approaching that special time of the year, no I’m not referring to the Holiday Season, but for all us in the HPC market, the big daddy of them all, SC 2019 takes place.  I’m excited that this year, my home state of Colorado will host this amazing event in Denver, where many visitors from around the world can see and experience the beauty of the Rocky Mountains and share in our Western hospitality.  Even more, is all that Bright Computing has planned for this event, and I hope that you will seriously consider making Bright Computing one of your stops and find out all that we have in store. Here is just a sampling of what you can expect:

Here at Bright, we were delighted to announce, earlier this week, that the University of North Dakota has deployed a host of Bright technology into its supercomputing infrastructure, including Bright Cluster Manager for HPC and Data Science, and Bright OpenStack. 

Last month, Bright had the opportunity to attend the Advanced Scale Forum as a bronze sponsor. As mentioned in an earlier blog post, the Advanced Scale Forum takes a broad look at the current state of the industry, discussing the modern challenges facing enterprise users as they leverage high-performance computing (HPC) to build and scale advanced computing solutions such as AI, Cloud, Containers, Data Analytics and so much more.

Everyone in the computer business knows just how hot the machine learning (ML) space is today.  The promise, as well as the demands, being placed on the AI data scientist by their companies, are numerous.  For most of these people, their GPU laden computers used to run analysis are viewed as just a tool. Often, each data scientist is provided with their own powerful computer and they don’t want to be burdened with the need to operationalize these computers. Simply put, they just want to run their jobs as quickly as possible.