the Bright blog

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Intersect360 Research Stat Sheet - Embracing the New Era of HPC

By Bright Staff | November 19, 2020

We’ve respected and worked with Addison Snell of Intersect360 for many years, so we were delighted that he was able to collaborate with us on a recent stat sheet project.

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AMAX and Bright - Spanning Geographies, Going from Strength to Strength

By Jack Hanna | November 18, 2020

With SC week upon us, it has been a great opportunity to catch up with our partners across the globe. I was compelled to give an update on our partner AMAX; a global leader in application-tailored solutions that enables its customers to solve complex computing challenges, meet product development demands, and integrate virtualized applications.

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Building a Cluster for a First-Time Sys Admin

Every once in a while, you face a situation that you just never expected. That was the case for Silicon Mechanics earlier this year.

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New Supercomputer “Ookami” Installed at Stony Brook, Underpinned By Bright

By Lee Carter | November 13, 2020

I was thrilled to read in the news today, that the Ookami computer at Stony Brook University has been launched and is now available to researchers nationwide as a testbed for exciting new high-performance computing technology. 

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So, You Want HPC and Virtualization? Let’s Talk about It!

By Bright Staff | November 10, 2020

We’d like to draw your attention to an article published today on The Next Platform. In the article, the author, David Gordon, explores the topic of HPC and virtualization, with an eye on an upcoming webinar that is due to take place next week on this very topic: 

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Dell EMC Ready Solutions for HPC Accelerate Customers' Time to Deliver New Products and Services

By Grant Gustafson | November 09, 2020

Here at Bright, we are honored to have such great partnership with companies like Dell Technologies. So, we were delighted to see their announcement today regarding two new Ready Solutions for HPC Genomics and Digital Manufacturing.

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Improving Capacity Planning for Important Environment Research with DHI Group

By Terry Rush | November 06, 2020

In case you missed the press release that we issued on Wednesday this week, the DHI Group -  a long-standing customer of Bright Computing - has once again chosen Bright technology to underpin its new compute environment. And, I’m pleased to say that the DHI Group will be putting us to good use. They will leverage the full-featured Bright Cluster Manager software platform to support pioneering environmental research projects; a matter close to all of our hearts.

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High Performance Clusters in the Cloud?

By Bill Harries | April 07, 2020

Now, why would I want to do that? Cloud resources can be expensive; I have to depend on a third party for some of my most important applications; performance may suffer; my data ends up outside my organization…all valid concerns. Which likely explains why most HPC continues to be hosted in-house. But another question is, is it even an option to effectively leverage the cloud for clustered computing? How do I extend my on-premise cluster to cloud servers and still manage them as a single cluster?

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How To Configure Bright Auto-Scaler to Achieve True Convergence of HPC and AI

By Robert Stober | September 17, 2019

Auto-scaling HPC and Kubernetes

Bright CEO Bill Wagner’s recent blog post “The Convergence of HPC and A.I.” pointed out that creating HPC and Kubernetes silos within a shared HPC infrastructure is more like “coexistence than convergence”. The solution I will be highlighting  is Bright Auto-scaler, which automatically resizes HPC and Kubernetes clusters (workload engines) according to workload demand and configured policies. This post describes how to configure Bright Auto-scaler to achieve true convergence of HPC and A.I. through a scenario.

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The Convergence of HPC and A.I.

By Bill Wagner | July 31, 2019

The convergence of HPC & A.I. is a hot topic right now, and for good reasons.  In practice, there is a symbiotic relationship between (for example) HPC simulations and machine learning.  HPC simulations generate tons of data, and as luck would have it, machine learning requires tons of data to train models.  If we turn the value proposition around, the quality and effectiveness of HPC simulations can be improved if we use machine learning to identify the parameters in simulation data that have a significant effect on the outcome and focus subsequent iterations of simulation on those parameters. 

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