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Big data challenges in simulation-based science

Data-related challenges are quickly dominating computational and data-enabled sciences, and are limiting the potential impact of scientific applications enabled by current and emerging high-performance distributed computing environments. These data-intensive application workflows involve dynamic coordination, interactions and data coupling between multiple application process that run at scale on different resources, and with services for monitoring, analysis and visualization and archiving. In this talk I will explore data grand challenges in simulation-based science and investigate how solutions based on data sharing abstractions, managed data pipelines, in-memory data-staging, in-situ placement and execution, and in-transit data processing can be used to address these data challenges at extreme scales.