The following is a guest post by Bill Bryce, Vice President of Products at Univa, a developer of workload management and optimization solutions.
Univa now offers support for Azure, enabling Univa customers to create dynamic clusters on demand in the cloud. Our product suite features the Univa Grid Engine resource manager, a proven, powerful, and integrated workload utilization solution for dynamically shared enterprise clusters, and the new Univa Grid Engine Container Edition product, which fully incorporates Docker containers into the Univa Grid Engine.
Azure is the best solution for running parallel apps in the cloud
With Azure, Microsoft makes it easy for our customers to run true HPC applications on demand in a cloud environment. With the power of Azure, Univa Grid Engine makes work management automatic and efficient and accelerates the deployment of cloud services, providing an excellent price/performance ratio. In addition, Univa solutions can run in Azure and schedule work in a Linux cluster built with Azure Virtual Machines.
More options and more performance for a better price
Our philosophy is to provide customers with the ability to choose which top provider works best for them, based on an organization's priorities. Adding Azure support increases our customers' flexibility. Univa's decision to include support for Microsoft Azure is great news for our mutual customers. With the resulting improvements in speed and usability, Univa is ideal for Azure customers who would benefit from an advanced workload management and scheduling solution.
Embracing Linux in the cloud, illustrating significant new openness
We all have the same objective: to offer customers the best solution, one that best satisfies their requirements. Univa is supporting Azure because it allows us to now leverage the unique Azure capabilities—true HPC capabilities in the cloud, on demand. By making use with the Azure platform a reality, Univa is satisfying customer demand for top performance at a competitive price. It allows customers to maximize use of computing resources, increasing cost-effectiveness.
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