High Performance Data Analytics (HPDA)
What is High Performance Data Analytics (HPDA)?
High performance data analytics (HPDA) refers to the use of high performance computing (HPC) to analyze large data sets for patterns and insights.
How does high performance data analytics work?
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.
What are the benefits of 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.