Cray Inc., a global supercomputer provider, today announced it has been awarded a contract from the Swiss National Supercomputing Centre (CSCS) in Manno, Switzerland, to acquire a Cray XMT supercomputer.
The announcement, made in conjunction with a CSCS-hosted workshop focused on large-scale data analysis, is Cray’s first order for its next-generation Cray XMT system.
CSCS is currently home to a Cray XT5 supercomputer nicknamed “Rosa” and was also the recipient of the first-ever Cray XE6 system. CSCS will use its next-generation Cray XMT supercomputer as part of a project called EUREKA, designed to address issues that require large-scale data analysis.
“The next generation of the Cray XMT supercomputer is purpose-built for performing real-time analysis of web-scale data,” Shoaib Mufti, director of knowledge management in Cray’s Custom Engineering group stated in the press release. “The system is ideal for analyzing dynamically changing data with complex relationships between time, space, events and communities, and excels at analytics tasks including pattern matching, scenario development, behavioral prediction, anomaly identification and graph analysis. The match between the needs of CSCS users and the strengths of the next-generation Cray XMT is an excellent fit, and we are very pleased that CSCS has signed the first contract for our new system.”
Dominik Ulmer, general manager at CSCS, noted the challenges that the Cray XMT are designed to overcome.
“Many researchers are faced with massive volumes of data through experiments, observations and simulations on a vast array of scientific applications such as material sciences, medicine genomics, high-energy physics, climate research and astrophysics. The next-generation Cray XMT will enable our scientists to perform data analysis applications that differ significantly from the current high performance computing workloads in that the data structures are often irregular (based on strings, trees, graphs and networks) without the high degree of spatial and temporal locality seen in physics-based simulations using regular matrices,” Ulmer stated.
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