What is High Performance Data Analytics?
High performance data analytics definition
High performance data analytics unites HPC with data analytics. The process leverages HPC’s use of parallel processing to run powerful analytics software at speeds higher than a teraflop, or a trillion floating-point operations per second. Through this approach, it is possible to quickly examine large data sets and draw conclusions about the information they contain.
Why high performance data analytics?
Some analytics workloads do better with HPC rather than standard compute infrastructure. While some “big data” tasks are intended to be executed on commodity hardware in a “scale out” architecture, there are certain situations where ultra-fast, high-capacity HPC “scale up” approaches are preferred. This is the domain of HPDA. Drivers include a sensitive timeframe for analysis, e.g., real time, high-frequency stock trading, and highly complex analytics problems found in scientific research.
HPE high performance data analytics
HPE offers data scientists the world’s most powerful, most efficient HPC solutions for HPDA workloads. Our HPC solutions are renowned for powering analytics at any scale with purpose-built technologies.
CIOs Exploit High Performance Computing to Boost Productivity and Competitiveness
Historically, most HPC systems in the private sector have been installed in dedicated HPC data centers for product development or other upstream R&D tasks. But in recent years, more businesses—many of them first-time HPC users—have integrated these systems into enterprise data centers to support complex business operations that enterprise server systems can't handle effectively alone.
Hyperion Research believes that with strong support from HPE, the new HPE Cray line of Shasta based systems is well positioned to benefit from, and to help drive, the robust growth we have projected for the worldwide HPC market.