Efficient parallel streaming algorithms for large-scale inverse problems

Large-scale inverse problems and uncertainty quantification (UQ), i.e., quantifying uncertainties in complex mathematical models and their large-scale computational implementations, is one of the outstanding challenges in computational science and will be a driver for the acquisition of future supercomputers. These methods generate significant amounts of simulation data that is used by other parts of the computation in a complex fashion, requiring either large inmemory storage and/or redundant computations. We present a streaming algorithm for such computation that achieves high performance without requiring additional in-memory storage or additional computations. By reducing the memory footprint of the application we are able to achieve a significant speedup (~3×) by operating in a more favorable region of the strong scaling curve.