High-Performance Computing's Role In Real-Time Graph Analytics
Data Science Central, Tuesday, January 30th, 2024
Relationship-rich graph structures can be quite complex and resource consuming to process at scale when using conventional technology. This is particularly the case when it comes to searches that demand the computation to reach 30 hops or more into the graphs.
Moreover, a key benefit of graph technology is ease of large-scale integration. When it comes to analytics, bringing all the relevant information together and processing it quickly is critical to effective discovery.
For that reason, high performance computing (HPC) methods that enable the processing of over a trillion floating point operations per second have been desirable for efficient, large=scale enterprise graph analytics. In 2012, for example, back in the early days of data lakes and rising demand for big data analytics, supercomputer provider Cray launched a subsidiary called YarcData that targeted the enterprise market for graph DBMSes.