Enterprise developers have several options for HPC software, including MapReduce platforms, stream-processing software, graph databases and high-performance, agent-based modeling systems. Each of these is well suited for particular types of HPC use cases and methodologies.


However, not all business problems are easily broken down into smaller problems. For example, after the marketing manager collects information on individual customers and their shopping patterns, she may want insight into connections among customers. Are there clusters of customers linked by social relationships that might influence product purchasing choices? This type of social network analysis is well suited for a graph database, such as Neo4j. Graph databases allow enterprises to easily model entities, such as customers and products, and relationships among those entities, such as customers’ social connections or the likelihood of products being sold together. The number of entities and relationships can be quite large in realistic problems and require high-performance computing hardware.

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