As demand for cost-effective energy and resource management continues to grow, intelligent automated building solutions are necessary to reduce energy consumption, increase alternative energy sources, reduce operational costs and find interoperable solutions that integrate with legacy equipment without massive investments in new equipment and tools. The ability to analyze, understand and predict building behavior offer tremendous opportunities to demonstrate and validate increased energy efficiencies, which may ease many particular exorbitant pressures taxing the grid. In this paper, we describe a research platform driven by an existing campus microgrid for developing large scale, predictive analytics for real-time energy management.

Petascale plasma physics simulations have recently entered the regime of simulating trillions of particles. These unprecedented simulations generate massive amounts of data, posing significant challenges in storage, analysis, and visualization. In this paper, we present parallel I/O, analysis, and visualization results from a VPIC trillion particle simulation running on 120,000 cores, which produces ~30TB of data for a single timestep. We demonstrate the successful application of H5Part, a particle data extension of parallel HDF5, for writing the dataset at a significant fraction of system peak I/O rates. To enable efficient analysis, we develop hybrid parallel FastQuery to index and query data using multi-core CPUs on distributed memory hardware. We show good scalability results for the FastQuery implementation using up to 10,000 cores. Finally, we apply this indexing/query-driven approach to facilitate the first-ever analysis and visualization of the trillion particle dataset.