Publications

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This document describes how to obtain, install, use, and enjoy a better life with OVIS version 2.0. The OVIS project targets scalable, real-time analysis of very large data sets. We characterize the behaviors of elements and aggregations of elements (e.g., across space and time) in data sets in order to detect anomalous behaviors. We are particularly interested in determining anomalous behaviors that can be used as advance indicators of significant events of which notification can be made or upon which action can be taken or invoked. The OVIS open source tool (BSD license) is available for download at ovis.ca.sandia.gov. While we intend for it to support a variety of application domains, the OVIS tool was initially developed for, and continues to be primarily tuned for, the investigation of High Performance Compute (HPC) cluster system health. In this application it is intended to be both a system administrator tool for monitoring and a system engineer tool for exploring the system state in depth. OVIS 2.0 provides a variety of statistical tools for examining the behavior of elements in a cluster (e.g., nodes, racks) and associated resources (e.g., storage appliances and network switches). It calculates and reports model values and outliers relative to those models. Additionally, it provides an interactive 3D physical view in which the cluster elements can be colored by raw element values (e.g., temperatures, memory errors) or by the comparison of those values to a given model. The analysis tools and the visual display allow the user to easily determine abnormal or outlier behaviors. The OVIS project envisions the OVIS tool, when applied to compute cluster monitoring, to be used in conjunction with the scheduler or resource manager in order to enable intelligent resource utilization. For example, nodes that are deemed less healthy, that is, nodes that exhibit outlier behavior in some variable, or set of variables, that has shown to be correlated with future failure, can be discovered and assigned to shorter duration or less important jobs. Further, applications with fault-tolerant capabilities can invoke those mechanisms on demand, based upon notification of a node exhibiting impending failure conditions, rather than performing such mechanisms (e.g. checkpointing) at regular intervals unnecessarily.

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This is meant as a place to put commentary on the whitepaper and is meant to be pretty much ad-hoc. Because the whitepaper describes a potential program in DOE ASCR and because it concerns many researchers in the field, these notes are meant to be extendable by anyone willing to put in the effort. Of course criticisms of the contents of the notes themselves are also welcome.

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Filtered mass density functions (FMDFs) of mixture fraction and temperature are studied by analyzing experimental data obtained from one-dimensional Raman/Rayleigh/LIF measurements of nonpremixed CH4/H2/N2 turbulent jet flames at Reynolds numbers of 15,200 and 22,800 (DLR-A and -B). The experimentally determined FMDFs are conditioned on the Favré filtered values of the mixture fraction and its variance. Filter widths are selected as fixed multiples of the experimentally determined dissipation length scale at each measurement location. One-dimensional filtering using a top-hat filter is performed to obtain the filtered variables used for conditioning. The FMDFs are obtained by binning the mass and filter kernel weighted samples. Emphasis is placed on the shapes of the FMDFs in the fuel-rich, fuel-lean, and stoichiometric intervals for the Favré filtered mixture fraction, and low, medium, and high values for the Favré filtered mixture fraction variance. It is found that the FMDFs of mixture fraction are unimodal in samples with low mixture fraction variance and bimodal in samples with high variance. However, the FMDFs of mixture fraction at the smallest filter size studied are unimodal for all values of the variance. The FMDFs of temperature are unimodal in samples with low mixture fraction variance, and either unimodal or bimodal, depending on the mixture fraction mean, in samples with high variance. The influence of the filter size and the jet Reynolds number on the FMDFs is also considered. © 2008 The Combustion Institute.

The goal of this research was to examine foundational methods, both computational and theoretical, that can improve the veracity of entity-based complex system models and increase confidence in their predictions for emergent behavior. The strategy was to seek insight and guidance from simplified yet realistic models, such as cellular automata and Boolean networks, whose properties can be generalized to production entity-based simulations. We have explored the usefulness of renormalization-group methods for finding reduced models of such idealized complex systems. We have prototyped representative models that are both tractable and relevant to Sandia mission applications, and quantified the effect of computational renormalization on the predictive accuracy of these models, finding good predictivity from renormalized versions of cellular automata and Boolean networks. Furthermore, we have theoretically analyzed the robustness properties of certain Boolean networks, relevant for characterizing organic behavior, and obtained precise mathematical constraints on systems that are robust to failures. In combination, our results provide important guidance for more rigorous construction of entity-based models, which currently are often devised in an ad-hoc manner. Our results can also help in designing complex systems with the goal of predictable behavior, e.g., for cybersecurity.

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One of the authors previously conjectured that the wrinkling of propagating fronts by weak random advection increases the bulk propagation rate (turbulent burning velocity) in proportion to the 4/3 power of the advection strength. An exact derivation of this scaling is reported. The analysis shows that the coefficient of this scaling is equal to the energy density of a lower-dimensional Burgers fluid with a white-in-time forcing whose spatial structure is expressed in terms of the spatial autocorrelation of the flow that advects the front. The replica method of field theory has been used to derive an upper bound on the coefficient as a function of the spatial auto-correlation. High precision numerics show that the bound is usefully sharp. Implications for strongly advected fronts (e.g., turbulent flames) are noted.

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