THE BRIGHT BLOG

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.

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. 

Having just returned to my home base in the UK after attending the 2^nd 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.   

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.

The historical thinking behind building and maintaining your own HPC cluster management solution using open source tools goes something like this:  “We have smart people that can build this. We have limited capital budget. Commercial cluster management software costs money, but open source tools like xCAT, Rocks, OpenHPC and others are free. If we build a solution ourselves using open source tools, we can use the savings to buy more hardware.”  

In a variety of industries, such as healthcare, smart retail outlets, manufacturing, agricultural, financial, and many other, trends in data collection, analysis, storage, and retrieval, have come together to make the Edge Computing one of the highest value propositions in recent years for many enterprises. However, the path to an Edge strategy that is both efficient and cost effective is often not easily found. IT organizations tasked with processing the influx of time-critical data generated by connected devices and applications at the edge are running into the challenge executing a solution that won’t increase network loads or limit the response to real-time insights.

High-Performance Computing has undoubtedly changed in the last few years and administrators are frequently tasked with supporting unusual request. Gone are the days of all HPC cluster users being script savvy Computer Scientists who speak BASH as fluently as they do English.

Data center managers are continually under pressure to deliver more results from their existing computing resources. One strategy that has proven successful is the aggregation of what have traditionally been computing silos into a modern, and efficient shared compute cluster.

In case you missed it, we’d like to draw your attention to a webinar that we hosted last week, giving an insight into Bright Cluster Manager 8.2. Catch the replay here.

While the world eagerly awaits the rollout of 5G networks and the exciting new apps for both consumers and businesses that will be made possible via 5G services, enterprises, telcos, hosting providers and others are scrambling to architect and build the computing infrastructure necessary to support the new requirements of these apps. Two key challenges in defining the computing infrastructure for 5G apps are that they will be data/compute intensive and have little tolerance for network latency. The former means that significant computing capacity is needed to deliver the intended experience of the app and the latter means that the compute must be located in relative proximity to the user of the app. The result: A significant build-out of localized compute (predominately Linux servers and clusters) will be required to service the new generation of 5G apps speeding towards us. But where (specifically) will that compute infrastructure reside?