Cloud News

Cray Supercomputers, Microsoft Azure Aid Healthcare Analytics

Healthcare analytics can benefit greatly and become more common from supercomputers being available in the cloud.

healthcare analytics

Source: Thinkstock

By Elizabeth O'Dowd

- Microsoft and Cray announced that Cray supercomputing systems will now be supported in Microsoft Azure datacenters. Users can now run heavy workloads such as advanced healthcare analytics and artificial intelligence (AI) in their Microsoft Azure environment.

Healthcare organizations looking to implement dense analytics or AI tools now have the opportunity to deploy these solutions in the cloud, along with other cloud-based infrastructure solutions. These tools can then contribute to analytics solutions easily because many of them collecting the data for analytics can also be deployed in Azure.

The demand for real-time analytics is also a factor in why healthcare organizations can benefit from more powerful solutions in the cloud.

Cray’s system architecture and Aries interconnect can handle the demand real-time analytics puts on a datacenter. Many organizations cannot afford to maintain or house a full Cray supercomputer on-premises. Putting the computing power into the cloud makes it more accessible to a wider range of organizations.

The availability of Cray supercomputers in Azure gives entities the ability to train AI deep learning models, which can be especially useful for medical imaging and research.

“Pharmaceutical and biotech scientists driving precision medicine discovery can now perform whole genome sequencing, shortening the time from computation to cure,” said Cray in its official release.

“Our partnership with Microsoft will introduce Cray supercomputers to a whole new class of customers that need advanced computing resources to expand their problem-solving capabilities, but want this new capability available to them in the cloud,” Cray President and CEO Peter Ungaro said in a statement.

“Dedicated Cray supercomputers in Azure not only give customers the breadth of features and services in enterprise cloud, but also the advantages of running a wide array of workloads on a supercomputer, the ability to scale applications to unprecedented levels, and the performance and capabilities previously only found in the largest on-premise supercomputing centers,” Ungaro continued.

“The Cray and Microsoft partnership is expanding the accessibility of Cray supercomputers and gives customers the cloud-based supercomputing capabilities they need to increase their competitive advantage.”

The Cray XC and Cray CS supercomputers with attached Cray ClusterStor storage systems will be available in select Microsoft Azure datacenters.

Cost and space are both challenges healthcare organizations face when upgrading and improving their health IT infrastructure. Making robust solutions available in the cloud saves on the staff needed to manage and maintain the technology, and also saves on the space needed to host solutions on-premises.

As AI and analytics technology advances, organizations have the opportunity to utilize computing that can provide clinicians with actionable data at the point of care.

The benefits of real-time data sharing and analytics are becoming clear for healthcare analytics and collaboration.

Many organizations are looking into health IT infrastructure solutions that support real-time data. True real-time analytics hasn’t been achieved yet, by near real-time analytics helps organizations overcome interoperability issues and provides patients with quicker results.

Real-time data lets clinicians collect, analyze, and decide on a patient’s condition during their initial interaction. Real-time environments lower costs by avoiding the bulk processing and the overnight loading into data warehouses.

Real-time environments also help with data governance, making sure the information entered is correct. If organizations can address data governance upfront, it solves a lot of problems concerning data quality.

However, integrating all the data needed for real-time analytics along with the computing power required to retrieve and analyze the data makes real-time data analytics difficult to achieve. Healthcare organizations are closer to being able to treat patients using real-time analytics by making supercomputers available in an accessible cloud environment.