Patterns for High Performance Computing and Big Data: State of the Art and Semantic Approaches

Because of the steadily growing volume and complexity of the data exchanged through the Internet and among connected devices, and the need to rapidly elaborate the incoming information, new challenges have been posed to High Performance Computing (HPC). Several architectures, programming languages, Cloud services and software have been proposed to efficiently deal with modern HPC applications, in particular with Big Data, and solve the related issues that arise. As a consequence, users often find it difficult to select the right platform, application or service fitting their requirements. In order to support them in deploying their applications, Patterns have been adopted and exploited, providing a base canvas on which users can develop their own applications. In this paper we offer an overview of modern Pattern advancements for HPC and Big Data, also providing an insight on semantic-based technologies which have been successfully applied to provide a flexible representation thereof.