Zuse Institute Berlin Uses DataDirect Networks to Accelerate Big Data Analytics for Advanced Scientific Research
Researchers in Northern Germany to Benefit from Deployment of World’s Fastest File Storage Technology with Increased Performance, Enterprise Reliability and Scalability
International Supercomputing Conference, DDN Booth #305 — Leipzig, Germany — June 17, 2013
- Zuse Institute Berlin (ZIB), a world-class research institute for applied mathematics and computer science, will use DataDirect Networks (DDN) in partnership with Cray Inc. as the foundation for its Big Data analytics and scientific research infrastructure for the HLRN supercomputer.
- ZIB will use DDNTM technology to perform massive scientific simulations, mathematical computations and analytics to accelerate research spanning the fields of physics, chemistry, fluid dynamics, engineering and the environment.
- With research driven by the principle of “Math, Computation & Data at Work”, ZIB supports scientific projects conducted by several hundred academic and government researchers who share access to distributed supercomputing resources in Berlin and Hannover, Germany, as part of the HLRN alliance.
- Given the wide range of data intensive applications and concurrent computations performed by the institute, HLRN required a Big Data solution that could deliver a significant increase in storage capacity, and also scale bandwidth and performance of its storage seamlessly. According to ZIB, DDN’s HPC portfolio, including the SFA12K® storage platform and GRIDScaler™ Parallel File Storage Appliance, outperformed competitive solutions in I/O price/performance ratio.
- Using the world’s fastest file storage technology from DDN, HLRN applications will be able to achieve an overall aggregate bandwidth performance of more than 50 gigabytes per second, which will represent a 3.6x improvement in performance over its previously deployed system. As a result, ZIB will be well positioned to expand its advanced scientific research while also embarking on new research that requires high-bandwidth access to extremely large data sets.
- ZIB works in close cooperation with partners from science, economy and society to develop leading-edge mathematical models and efficient algorithms.
DDN Delivers Capacity-Based Scale and Performance for Industry-Leading TCO
- Over the past two years, ZIB experienced a major surge in Big Data growth, which drove the need for greater disk capacity and bandwidth for rapid access to large scale datasets. The DDN SFA12K storage platform, deployed with DDN GRIDScaler parallel file system, serves as the backbone for the HLRN-III peta-scale computing resource, ensuring the optimal balance between compute power and storage bandwidth.
- With DDN’s policy-driven snapshots, ZIB meets a crucial requirement for providing an extra measure of protection against accidental data deletion, data corruption or viruses.
- ZIB also is impressed with DDN’s ability to reduce energy consumption by eliminating unnecessary storage enclosures, switches, fans and power supplies to help reduce data center, power and cooling requirements for lowered Total Cost of Ownership (TCO).
- Working in partnership with Cray Inc. to deploy the system, DDN will assist ZIB in migrating up to 1.6 petabytes of vital HLRN project data, encompassing up to 800TBs at each HLRN production site, while providing ample capacity to scale its storage system to 40PBs as future needs dictate without having to add more components.
Dr. Thomas Steinke, leader of the supercomputing algorithms and consultancy group at Zuse Institute Berlin (ZIB):
- “As a research institute focused on mathematics and computer science and resource provider in the HLRN alliance, it’s crucial for us to balance compute power and storage bandwidth, which isn’t easy. With its installation, DDN will deliver an exceptional improvement in bandwidth performance over our current system while also outranking the competing solutions in terms of price/performance.”
- “With DDN technology, ZIB will be able to increase storage bandwidth and performance to enable more modeling and simulations, as well as apply cutting-edge algorithms to answer new questions that can lead to major scientific breakthroughs.”
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