Nor-Tech Integrates Bright Cluster Manager for Data Science into HPC Technology

By Bright Staff | May 13, 2019

Nor-Tech, a leading HPC technology integrator and Bright Premier Reseller has recently announced they are now building computers that are optimized with Bright Cluster Manager for Data Science. Bright Cluster Manager for Data Science provides Nor-tech clients with an intuitive management interface, enabling them to administer data science clusters as a single entity, provisioning the hardware, operating system, big data, and deep learning (DL) software from a single interface. The intuitive Bright management interface monitors virtually every aspect of every node reporting any problems it detects in the software or hardware so that administrators can act on any issue impacting data science cluster performance.

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The Top 5 Benefits That Our Customers Value Most In Our Product

By Bill Wagner | May 10, 2019

I recently completed my third year as the CEO of Bright Computing, and it seems that a day doesn’t go by without me discovering some new (to me) capability within our product, Bright Cluster Manager.  With more than a decade of development and customer implementations under its belt, coupled with a product team of 40 people working on the product every day, it’s obviously hard to keep up with everything this product can do.  But while the product has grown in capability, it has remained true to its reputation for being easy to use. 

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Bright for DGX Clusters

By Bill Wagner | April 30, 2019

At the 2019 GPU Technology Conference in March, NVIDIA Founder & CEO Jensen Huang positioned NVIDIA DGX servers at the intersection of scale-up and scale-out architectures, sitting squarely in the sweet spot of data science driven by the combination of increased concurrency of data science workloads and the massive compute requirements associated with those workloads.  As a standalone server, the DGX delivers a solid scale-up architecture for data/compute-intensive workloads, and NVIDIA’s announced acquisition of Mellanox with its high-speed interconnects will only enhance that position and help enable a new realm of scale-out architectures as well. 

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Why pay for a second monitoring solution when you can use Bright?

By Robert Stober | April 19, 2019

One of Bright Cluster Manager’s most popular features is its built-in monitoring system. It is lightweight and efficient, and it works right out of the box. But what people generally don’t know is that they can use Bright to monitor non-Bright nodes; nodes that were neither provisioned by, nor managed by, Bright. The Bright Lightweight CMDaemon can be used to monitor and healthcheck auxiliary servers that support the cluster but aren’t part of it, for example, database servers, authentication servers, and file servers.

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"Getting Bright!" - in 2 minutes

By Bill Harries | April 12, 2019

I recognize that most readers of this blog are pretty well versed in the complexities of managing large scale clusters for high performance computing, artificial intelligence, etc.  But it might not be so obvious to your colleagues or others that need to understand why commercial-grade software with enterprise-class support is so critical to efficiently deploying and managing these complex systems.  Or why managing geographically disbursed clusters is so challenging.  Or why understanding who is using the cluster resources and how allocating those resources efficiently is crucial to the ROI of these very large investments in hardware, applications, and data centers. 

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Bright Joins Dell EMC at Rice Oil & Gas and GTC

By Lori Martin | April 09, 2019

It was a great privilege for Bright to be an invited guest of Dell EMC at two recent industry events.

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Automating tasks with Ansible on Bright Cluster Manager head nodes and software images.

By Bright Engineering Staff | April 02, 2019

Ansible is a simple, agentless IT automation tool that anyone can use. In this blog post, we will look at automating head node and software image tasks using Ansible.

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Homegrown Cluster Management...Just because you can, doesn't mean you should Pt. 2

By Bill Wagner | March 28, 2019

Picking up from last week, I illustrated a common dialogue between IT administrators and executive leadership concerning the decision to build an HPC cluster management solution. Now that the green light has been given to build, all of that money you “saved” by not using commercial cluster management software allowed you to buy more hardware to support jobs from end users, right?  You can see the extra servers on the floor, so you must be providing your users with more capacity for work, right?  Maybe not. Does the do-it-yourself approach you developed tell you precisely which system resources are actually being used by end users, and for which jobs?  Or, are users requesting more resources for their jobs than they really need and sitting on them (unused), preventing other users from gaining access to do real work?  And one more thing … how much server/system resource is being inefficiently consumed by the processes of your do-it-yourself cluster management solution at the expense of real work for users?  The point is, that cluster that appears to be 95% utilized is very likely to be far less productive than you think. 

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It all started in Tokyo - Bright's growing business in Asia

By Lee Carter | March 26, 2019

Having just returned to my home base in the UK after attending the 2nd running of Supercomputing Asia, where I had the pleasure of meeting and speaking with many of Bright’s customers and partners attending the show, I started thinking about how our business got started in the Region.   

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It’s time to throw both IT and the AI Data Scientist a life preserver!

By Dan Kuczkowski | March 21, 2019

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.

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