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Moving HPC to the Cloud with Easy8 and Bright Cluster Manager

In this video from SC19, Bill Wagner from Bright Computing announces the latest release of Bright Cluster Manager software for HPC, OpenStack, and Data Science. Now available for free for clusters up to eight nodes with the Easy8 program, Bright Cluster Manager simplifies building and managing Linux clusters from the core to the cloud and to the edge. “We are excited about the new features in 9.0,” said Bill Wagner, CEO of Bright Computing. “Our development team has been hard at work implementing a host of features designed to better extend on-premise clusters to the public cloud and edge, improve ease of use, lower administrative costs, and increase standardization across the enterprise.”

Bright Cluster Manager now Free for Clusters up to 8 Nodes

Today Bright Computing announced the launch of “Easy8” – a program designed to put their award-winning Bright Cluster Manager software in the hands of every organization working with high-performance Linux clusters. Easy8 offers the full-featured Bright Cluster Manager software for up to 8 nodes at no charge and is available immediately from the Bright Computing website. Bright also announced the launch of its new “BEACON” user community, which is designed to provide Bright customers and Easy8 users with a free forum where they can access technical content, share experiences, ask/answer questions, exchange ideas, and suggest product enhancements.

Bright Reseller Raid Media Systems Targets German Supercomputing Market

Raid Media Systems has become the latest partner to sign up to the Bright Computing Reseller Program. As a reseller, Raid Media Systems has chosen to partner with Bright Computing in order to provide its customers with an easy to use cluster management solution. The Reseller anticipates that Bright technology will allow its staff to spend less time on cluster management tasks and more time focusing on improving application performance and cluster utilization for its customers. “We are happy to partner with Bright to offer industry-proven cluster management software to our customer base,” said Daniel Vogel, Systems Engineer at Raid Media Systems. “This ensures that our customers get the very best solutions for their needs.”

Exploring Bright Cluster Manager on NVIDIA DGX Systems

Artificial Intelligence (AI) is rapidly becoming an essential business and research tool, providing valuable new insights into corporate data and delivering those insights with high velocity and accuracy. While these AI capabilities add significant value to our lives, they are the most demanding workloads in modern computing history. Download the new report from Bright Computing to explore how NVIDIA DGX and Bright Cluster Manager are designed to deliver more of what AI infrastructure users need. 

Bright Cluster Manager Prepares for Exascale at SC18


Bright’s exascale-capable version of Bright Cluster Manager is designed to support 100,000+ nodes. The company began delivering enhancements towards exascale in 2016 with features such as dedicated provisioning nodes, and a new monitoring subsystem designed for extreme scale. Current development work includes dedicated monitoring nodes, hierarchical object rendering in the Bright UI, optimized API communication patterns, and exascale simulation testing.”

Bright Computing adds support for OpenHPC

Today Bright Computing announced it has joined the Linux Foundation and will participate in the OpenHPC Community project. “Many of our HPC customers incorporate both commercial and open source management regimens on clusters based on Intel Xeon Scalable processors,” said Trish Damkroger, Vice President and General Manager, Technical Computing Initiative at Intel Corporation. “By supporting OpenHPC packages in their software, Bright Computing will help enable HPC practitioners assemble the ideal management framework for their needs.”

Resource Management Across the Private/Public Cloud Divide

This is the final entry in a insideHPC series of features that explores new resource management solutions for workload convergence, such as Bright Cluster Manager by Bright Computing. This article highlights how resource management systems that can manage clusters on-premises or in the cloud greatly simplify cluster management. That way, different tools do not have to be learned for managing a cluster based on whether it is located in the company data center or in the cloud.

HPC System Management: Scheduling to Optimize Infrastructure

Whether the application is floating-point intensive, integer based, uses a lot of memory, has significant I/O requirements, or its widespread use is limited by purchased licenses, a system that assigns the right job to the right server is key to maximizing the computing infrastructure. We continue our insideHPC series of features exploring new resource management solutions for workload convergence, such as Bright Cluster Manager by Bright Computing. This article discusses how scheduling can work to optimize infrastructure and improve HPC system management. 

The 3 Types of Cloud Computing for HPC Systems Management

Three types of clouds have been developed: public clouds, private clouds and hybrid clouds. We continue our insideHPC series of features exploring new resource management solutions for workload convergence, such as Bright Cluster Manager by Bright Computing. This article explores the three types of cloud computing, as well as how cloud computing can work to reduce CAPEX. 

Bright Computing Release 8.1 adds new features for Deep Learning, Kubernetes, and Ceph

Today Bright Computing released version 8.1 of the Bright product portfolio with new capabilities for cluster workload accounting, cloud bursting, OpenStack private clouds, deep learning, AMD accelerators, Kubernetes, Ceph, and a new lightweight daemon for monitoring VMs and non-Bright clustered nodes. “The response to our last major release, 8.0, has been tremendous,” said Martijn de Vries, Chief Technology Officer of Bright Computing. “Version 8.1 adds many new features that our customers have asked for, such as better insight into cluster utilization and performance, cloud bursting, and more flexibility with machine learning package deployment.”