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Creators/Authors contains: "Kellar, Samuel"

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  1. ; ; ; ; (, Crystals)
    Machine learning approaches have recently been applied to the study of various problems in physics. Most of these studies are focused on interpreting the data generated by conventional numerical methods or the data on an existing experimental database. An interesting question is whether it is possible to use a machine learning approach, in particular a neural network, for solving the many-body problem. In this paper, we present a neural network solver for the single impurity Anderson model, the paradigm of an interacting quantum problem in small clusters. We demonstrate that the neural-network-based solver provides quantitative accurate results for the spectral function as compared to the exact diagonalization method. This opens the possibility of utilizing the neural network approach as an impurity solver for other many-body numerical approaches, such as the dynamical mean field theory. 
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  2. ; ; ; (, 2018 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW))
    Overheads associated with fine grained communication in task based runtime systems are one of the major bottlenecks that limit the performance of distributed applications. In this research, we provide methodology and metrics for analyzing network overheads using the introspection capabilities of HPX, a task based runtime system. We demonstrate that our metrics show a strong correlation with the overall runtime of our test applications. Our aim is to eventually use these metrics to tune, at runtime, parameters relating to active message coalescing. This method improves on the postmortem analysis techniques that are currently employed to tune network settings in distributed applications. 
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