This account is pending registration confirmation. Please click on the link within the confirmation email previously sent you to complete registration. Need a new registration confirmation email? Click here
SUNNYVALE, Calif., and
Jan. 9, 2013 /PRNewswire/ --
Panasas, Inc., the leader in high performance parallel storage for technical computing applications and big data workloads, today announced that the UK's University of
Nottingham has upgraded its high performance computing (HPC) center with Panasas ActiveStor 12 storage in a 240 terabyte deployment. The new cluster is used by numerous departments across the university, including computer science, pharmacy and engineering.
"We are delighted that the University of
Nottingham chose Panasas to satisfy its HPC storage requirements," said
Barbara Murphy, chief marketing officer at Panasas. "ActiveStor gives the university unmatched performance, scalability and reliability without complex and time-consuming system management. We look forward to continuing to work with the university, as well as our many other academic customers in the region."
The University of
Nottingham, ranked in the UK's top 10 in the Shanghai Jiao Tong (SJTU) World University Rankings and within the top 100 in the QS World University Rankings, first upgraded to Panasas in 2007 when it purchased an ActiveStor 7 solution to overcome performance problems associated with its previous storage system.
"We saw a big improvement in performance with the acquisition of Panasas ActiveStor," said
Chris Booth, senior systems developer. "Also, our previous storage system went down about once a month. ActiveStor has never gone down – ever."
Researchers in the Physical and Theoretical Chemistry Department, whose work includes the simulation of proteins to understand diseases and enable the development of drugs to help fight or prevent them, are among the most demanding users of the HPC center. Their simulation of the motion of proteins is a complex task that can involve trillions of time-steps to map each movement of every protein, requiring a high-performance compute cluster and parallel storage.