As researchers in all major science domains struggle to keep up with the exponentially growing amount of digitally-based data, the HPC (high-performance computing) community will evolve to include HPD, or high-performance data, to benefit researchers who need to access, analyze and store extremely large data sets in significantly shorter amounts of time.

“We are figuring out ways to fuse HPC together with what I now call HPD, and put the best of both worlds into one computer system,” said Michael Norman, interim director of the San Diego Supercomputer Center (SDSC) at UC San Diego, during a presentation this week as part of the University’s activities complementing the American Association for the Advancement of Science (AAAS) annual meeting in San Diego.
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“It’s not just about FLOPS (floating point operations per second) anymore as a key hardware metric,” said Norman, who is a distinguished professor of physics at UC San Diego and a noted astrophysicist in addition to leading SDSC. “It’s also about IOPS, or I/O operations per second. We expect Gordon to have a rating of about 35 million IOPS, which would be a record. That makes it ideal for tackling data-intensive problems that don’t scale well on today’s massively parallel supercomputers, such as the analysis of individual genomes to tailor drugs to specific patients, developing more accurate models to predict the impact of earthquakes or other natural disasters on buildings and other structures, and coupled ocean/atmospheric simulations that offer greater insights into what’s happening to the planet’s climate.”

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