Publications

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The metrics used for evaluating energy saving techniques for future HPC systems are critical to the correct assessment of proposed methods. Current predictions forecast that overcoming reduced system reliability, increased power requirements and energy consumption will be a major design challenge for future systems. Modern runtime energy-saving research efforts do not take into account the energy spent providing reliability. They also do not account for the increase in the probability of failure during application execution due to runtime overhead from energy saving methods. While this is very reasonable for current systems, it is insufficient for future generation systems. By taking into account the energy consumption ramifications of increased runtimes on system reliability, better energy saving techniques can be developed. This paper demonstrates how to determine the impact of runtime energy conservation methods within the context of failure-prone large scale systems. In addition, a survey of several energy savings methodologies is conducted and an analysis is performed with respect to their effectiveness in an environment in which failures occur.

Stochastic unit commitment models typically handle uncertainties in forecast demand by considering a finite number of realizations from a stochastic process model for loads. Accurate evaluations of expectations or higher moments for the quantities of interest require a prohibitively large number of model evaluations. In this paper we propose an alternative approach based on using surrogate models valid over the range of the forecast uncertainty. We consider surrogate models based on Polynomial Chaos expansions, constructed using sparse quadrature methods. Considering expected generation cost, we demonstrate that the approach can lead to several orders of magnitude reduction in computational cost relative to using Monte Carlo sampling on the original model, for a given target error threshold.

Stochastic unit commitment models typically handle uncertainties in forecast demand by considering a finite number of realizations from a stochastic process model for loads. Accurate evaluations of expectations or higher moments for the quantities of interest require a prohibitively large number of model evaluations. In this paper we propose an alternative approach based on using surrogate models valid over the range of the forecast uncertainty. We consider surrogate models based on Polynomial Chaos expansions, constructed using sparse quadrature methods. Considering expected generation cost, we demonstrate that the approach can lead to several orders of magnitude reduction in computational cost relative to using Monte Carlo sampling on the original model, for a given target error threshold.

Power and energy concerns are motivating chip manufacturers to consider future hybrid-core processor designs that may combine a small number of traditional cores optimized for single-thread performance with a large number of simpler cores optimized for throughput performance. This trend is likely to impact the way in which compute resources for network protocol processing functions are allocated and managed. In particular, the performance of MPI match processing is critical to achieving high message throughput. In this paper, we analyze the ability of simple and more complex cores to perform MPI matching operations for various scenarios in order to gain insight into how MPI implementations for future hybrid-core processors should be designed.

We develop stochastic mixed finite element methods for spatially adaptive simulations of fluid-structure interactions when subject to thermal fluctuations. To account for thermal fluctuations, we introduce a discrete fluctuation-dissipation balance condition to develop compatible stochastic driving fields for our discretization. We perform analysis that shows our condition is sufficient to ensure results consistent with statistical mechanics. We show the Gibbs-Boltzmann distribution is invariant under the stochastic dynamics of the semi-discretization. To generate efficiently the required stochastic driving fields, we develop a Gibbs sampler based on iterative methods and multigrid to generate fields with O(N) computational complexity. Our stochastic methods provide an alternative to uniform discretizations on periodic domains that rely on Fast Fourier Transforms. To demonstrate in practice our stochastic computational methods, we investigate within channel geometries having internal obstacles and no-slip walls how the mobility/diffusivity of particles depends on location. Our methods extend the applicability of fluctuating hydrodynamic approaches by allowing for spatially adaptive resolution of the mechanics and for domains that have complex geometries relevant in many applications. © 2014 Elsevier Inc.

We use Sandia's Z machine and magnetically accelerated flyer plates to shock compress liquid krypton to 850 GPa and compare with results from density-functional theory (DFT) based simulations using the AM05 functional. We also employ quantum Monte Carlo calculations to motivate the choice of AM05. We conclude that the DFT results are sensitive to the quality of the pseudopotential in terms of scattering properties at high energy/temperature. A new Kr projector augmented wave potential was constructed with improved scattering properties which resulted in excellent agreement with the experimental results to 850 GPa and temperatures above 10 eV (110 kK). Finally, we present comparisons of our data from the Z experiments and DFT calculations to current equation of state models of krypton to determine the best model for high energy-density applications.

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