Cray unveils dedicated big data analytics platform

Cray Urika-GX system is first platform from HPC specialist designed for big data workloads

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Cray unveils dedicated big data analytics platform The Cray Urika-GX system is intended as an agile analytics platform to handle multiple big data workloads.
By  Mark Sutton Published  May 25, 2016

HPC specialist Cray has released its first dedicated supercomputing platform for big data analytics.

The Cray Urika-GX system is intended as an agile analytics platform which combines supercomputing technology with an open, enterprise-ready software framework. The Urika-GX is capable of running multiple analytics workloads concurrently on a single platform, with supercomputing speed and scalability.

Optimized for demanding analytics workloads, the Cray Urika-GX system is pre-tested and pre-integrated with the Hortonworks Data Platform providing Hadoop and Apache Spark, as well as the Cray Graph Engine, designed for solving the largest and most complex graph analytics problems. The system includes enterprise tools, such as OpenStack for management and Apache Mesos for dynamic configuration - all designed to protect customers' investments in the rapidly-changing big data software landscape.

"The Urika-GX is a dynamic analytics solution that brings out the best of Cray's decades of expertise in providing our customers with world-class systems for data-intensive computing," said Peter Ungaro, president and CEO of Cray. "Customers have asked us to blend the unique features of our product lines into a single platform for data analytics. We took the Aries system interconnect from our supercomputers, the industry-standard architecture of our clusters, the scalable graph engine from the Urika-GD appliance, and the pre-integrated, open infrastructure of our Urika-XA system and combined them into one agile analytics platform. The Urika-GX gives our customers the tool they need to overcome their most advanced analytics challenges today, and the platform to bridge to tomorrow."

Cray Urika-GX systems are currently being used by customers in life sciences, healthcare, and cybersecurity industries, along with The Broad Institute of MIT and Harvard, which is using the system for analyzing high-throughput genome sequencing data.

"With the Cray Urika-GX, we had quality score recalibration results from our Genome Analysis Toolkit (GATK4) Apache Spark pipeline in nine minutes instead of forty minutes," said Adam Kiezun, GATK4 Project Lead at the Broad Institute. "This highlights the potential to accelerate delivery of genomic insights to researchers who are making breakthroughs in the fight against disease."

An exclusive feature of the Cray Urika-GX system is the Cray Graph Engine for fast, complex iterative discovery. Graph analytics has long been understood to pose some of the most difficult scaling and performance challenges for modern analytics systems. The Cray Graph Engine on the Urika-GX system, originally developed for the Cray Urika-GD Graph Discovery appliance, is typically ten to 100 times faster than current graph solutions for complex analytics operations. The Cray Graph Engine can run at any scale from a single processor to thousands of processors without compromising performance. With the Cray Graph Engine, customers can tackle multi-terabyte datasets comprised of billions of objects. The Cray Graph Engine can run in conjunction with open analytics tools such as Hadoop and Spark, enabling customers to build complete end-to-end analytics workflows and avoid unnecessary data movement.

The Cray Urika-GX system features Intel Xeon Broadwell cores, 22 terabytes of memory, 35 terabytes of local SSD storage capacity, and the Aries supercomputing interconnect, which provides the unmatched network performance necessary to solve the most demanding big data problems. Three initial enterprise-accessible configurations featuring 16, 32, or 48 nodes delivered in an industry standard 42U 19-inch rack will be available in Q3 2016, and larger configurations will be available in the second half of 2016.

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