What is High Performance Data Analytics?
High performance data analytics definition
High performance data analytics unites HPC with data analytics. The process harnesses 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.
HYBRID HIGH PERFORMANCE COMPUTING CLOUD
With pressure mounting to deliver and support higher-quality products and services through digital platforms, organisations are looking to high performance data analytics, artificial intelligence and deep learning, and other high performance computing workloads. Public cloud and private cloud both have their drawbacks, but HPE’s hybrid HPC solution blends the best of both worlds. Read about:
- What are hybrid cloud and hybrid HPC?
- The 3 key challenges faced by enterprises with HPC.
- Examples of hybrid HPC cloud implementations.