THE BRIGHT BLOG

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.

Even though Bright Cluster Manager provides a fantastic monitoring environment for high performance computing, AI, data science, big data, OpenStack, machine and deep learning environments, it’s important to recognize that a Bright cluster is not an island in an organization's IT infrastructure. Many organizations will have infrastructure-wide monitoring and reporting environments in place, in addition to Bright’s capabilities. We also have to accept that there may be companies that wish to use monitoring tools other than Bright View to see monitoring data.

For the majority of our customers, the usual way of installing a Bright cluster is by downloading the Bright ISO, going through a head node installation, and then PXE booting your compute nodes to allow them to join the cluster.

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?

Whether you are a Bright customer, a Bright reseller, or in the process of evaluating Bright, you will be aware that Bright Cluster Manager is great at monitoring your cluster. From the moment that you set up your cluster for the first time, Bright’s monitoring capabilities set to work, providing comprehensive metrics and alerts.

To extract the most value from your HPC cluster, you need to ensure that system resources are being properly utilized.  HPC system users are notorious for over-requesting resources for their jobs, resulting in idle or underutilized resources that could otherwise be doing work for other jobs.  While one reason for this can be users hoarding resources to ensure they have what they need, another common reason why users over request resources is that they simply don’t know what resources their jobs will need to complete the job in a specified time.  For administrators to ensure that their precious and expensive cluster resources aren’t being squandered, they need to get actionable details regarding how the resources are being used.  More specifically, they need to know things like which jobs are using which resources, which jobs aren’t using resources that they’ve provisioned and which users are repeatedly hoarding resources unnecessarily, as well as other things.

Exporting and importing Bright objects is a topic that comes up very often, and we have an exciting enhancement on the horizon that I wanted to tell you about.

At a time when many of our customers are asking their employees to work from home in order to minimize spreading the COVID-19 virus, the challenge of maintaining business continuity with a remote workforce is a new endeavor for most organizations.  As such, we thought this would be a good opportunity to remind everyone about a feature in Bright Cluster Manager that makes it possible for cluster administrators to remotely access and manage their systems.

Earlier this week, Bright announced that Bright Cluster Manager for Data Science is now available at no charge as part of the Easy8 program. Easy8 was launched in November of 2019 and is designed to put Bright’s award-winning cluster management software in the hands of every organization working with high-performance Linux clusters.

As one of the newer members of the Bright team, I continue to be impressed by the overall level of happiness and satisfaction that emanates from our customer base.