Computer Science, Machine Learning, and Applied Mathematics
TimeMonday, June 2614:00 - 16:00 CEST
DescriptionComputing at large scales has become extremely challenging due to increasing heterogeneity in both hardware and software. A positive feedback loop exists where more scientific insight leads to more complex solvers which in turn need more computational resource. More and more scientific workflows need to tackle a range of scales and use machine learning (ML) and artificial intelligence (AI) intertwined with more traditional numerical modeling methods, placing more demands on computational platforms. These constraints indicate a need to fundamentally rethink the way computational science is done and the tools that are needed to enable these complex workflows. It is not obvious that current set of C++ based solutions will suffice, or that relying exclusively upon C++ is the best option, especially because several newer languages and boutique solutions offer more robust design features to tackle the challenges of heterogeneity. This two part minisymposium will include presentations about languages and heterogeneity solutions that are not tied to C++, and offer options beyond template metaprogramming and parallel-for for performance and portability. One slot will be reserved for open discussion and exchange of ideas.