Bright Computing Announces Integration with IBM Power Systems

Bright Computing, a global leader in cluster and cloud infrastructure automation software, today announced that Bright Cluster Manager 8.0 now integrates with IBM Power Systems™. 

[easy-tweet tweet=”IBM Power servers are built for modern data solutions” hashtags=”IBM, Data “]

IBM Power Systems high-performance computing servers are deployed in many of the largest clusters around the world. Configurable into highly scalable Linux® clusters, Power Systems offer extreme performance for demanding workloads such as genomics, finance, computational chemistry, oil and gas exploration, and high-performance data analytics. IBM Power servers are built for modern data solutions and promote fast time to insights with solution-specific hardware accelerators, scaling-out as the data requirement grows.

Unveiled in May 2017, Bright Cluster Manager 8.0 delivers exciting new features for automation and ease-of-use for Linux-based clusters and public, private and hybrid clouds. Feature highlights include a new and highly intuitive web-based user interface, integration with Apache Mesos and Marathon, support for OpenStack Newton, and for the first time, support for IBM Power Systems servers.

Martijn de Vries, CTO at Bright Computing, commented; “At Bright, we pride ourselves on an aggressive development strategy to maintain our position as a leading provider of infrastructure management technology. Our customers are increasingly looking at POWER8, and are therefore we are very happy to be able to offer this valuable integration as part of Bright Cluster Manager 8.0.”

“The integration of Bright Cluster Manager 8.0 with IBM Power Systems has created an important new option for users running complex workloads involving high-performance data analytics,” said Sumit Gupta, VP, HPC, AI & Machine Learning, IBM Cognitive Systems. “Bright Computing’s emphasis on ease-of-use for Linux-based clusters within public, private and hybrid cloud environments speaks to its understanding that while data is becoming more complicated, the management of its workloads must remain accessible to a changing workforce.”

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