Student Posters Repository

Making modern high-performance architectures accessible to specific data analytics needs has become a field of research on its own. This naturally entails tackling performance engineering issues, where hardware characteristics can help the tuning of algorithms and data structures to fulfill the needs of real-life applications. Machine learning provides a vast amount of scenarios that deal with large amounts of data that need efficient algorithms and data structures, making it perfectly suited for a high-performance treatment. Additionally, input data and problem parameters are often uncertain, needing a complex treatment. Adaptive sparse grids, which are increasingly being used in numerous fields of study, provide a very powerful tool in reducing complexity by tackling the issues of high-dimensional spaces. In the context of data mining, sparse grids have already been successful as a dimensional-aware alternative to more traditional approaches and are especially well suited to modern multi- and many-core systems.

In that sense, the focus of this work is on developing sparse grid algorithms optimized for the latest high-performance clusters. These codes are applied to solving several computationally-demanding problems in machine learning and other fields that lend themselves to a sparse grid treatment, in both deterministic and stochastic scenarios.