A Bayesian Reliability Analysis of Neutron-Induced Errors in High Performance Computing Hardware

Storlie, Curtis B.; Michalak, Sarah; Quinn, Heather; DuBois, Andrew; Wender, S. A.; DuBois, D. F.

doi:10.1080/01621459.2013.770694

Cited by 8 publications

(6 citation statements)

References 31 publications

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“…We include the additional covariates for gender, education, and presence of an APOE-ε4 allele on the state transition rates, and introduce the emitted response variables associated with amyloid and cortical thickness to allow for agnostic biomarker cut-points. Additionally, we consider the emitted re- models (Storlie et al 2013, George & McCulloch 1993. Other options include spike and slab priors (Ishwaran et al 2005), or computing competing models and comparing them with some criterion such as the Widely Applicable Information Criterion (WAIC, see Watanabe (2010)).…”

Section: Discussionmentioning

confidence: 99%

“…require some decisions about which covariates to include in which equations. Stochastic search variable selection is a viable option as it has been used successfully on event rate models (Storlie et al 2013, George & McCulloch 1993. Other options include spike and slab priors (Ishwaran et al 2005), or computing competing models and comparing them with some criterion such as the Widely Applicable Information Criterion (WAIC, see Watanabe (2010)).…”

Section: Discussionmentioning

confidence: 99%

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A Bayesian Approach to Multistate Hidden Markov Models: Application to Dementia Progression

Williams

Storlie

Therneau

et al. 2019

Journal of the American Statistical Association

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People are living longer than ever before, and with this arises new complications and challenges for humanity. Among the most pressing of these challenges is of understanding the role of aging in the development of dementia. This paper is motivated by the Mayo Clinic Study of Aging data for 4742 subjects since 2004, and how it can be used to draw inference on the role of aging in the development of dementia. We construct a hidden Markov model (HMM) to represent progression of dementia from states associated with the buildup of amyloid plaque in the brain, and the loss of cortical thickness. A hierarchical Bayesian approach is taken to estimate the parameters of the HMM with a truly time-inhomogeneous infinitesimal generator matrix, and response functions of the continuous-valued biomarker measurements are cut-point agnostic. A Bayesian approach with these features could be useful in many disease progression models. Additionally, an approach is illustrated for correcting a common bias in delayed enrollment studies, in which some or all subjects are not observed at baseline. Standard software is incapable of accounting for this critical feature, so code to perform the estimation of the model described below is made available online.We formalize the discrete-state space exhibited in Figure 1 in which many of the states are defined by continuous biomarkers. The previous work of Jack et al. (2016) defined a state space similar to Figure 1, but in which the high/low burden biomarker states were defined by practitioner chosen, hard biomarker cut-points. Hard cut-points for discretizing continuous measurements of biological processes are practically and philosophically problematic, and have to be chosen more or less arbitrarily.Moreover, we illustrate a general and effective framework for fitting a continuoustime, discrete-state HMM within the Bayesian paradigm, and the infinitesimal generator matrix of the underlying Markov process is allowed to be truly time-inhomogeneous (as a function of an individual's age). Time must be treated as continuous because, as in much of medical research, subjects are often observed irregularly in time.Our final contribution is that in addition to the effect of age, the effects of the covariates gender, number of years of education, and presence of an APOE-ε4 allele on the infinitesimal transition rates are also estimated. The importance of these variables has been well documented in the medical literature but their effect on aging has not been studied in this context (i.e., how they affect the transition rates between states in Figure 1). In addition to the new insights these features bring to the medical community, flexible software to fit the models described below is provided at https://jonathanpw.github.io/software.html.Our analysis builds on the work of Jack et al. (2016) with more sophisticated modeling which allows for deeper insights. They found that a Markov model of disease progression for dementia is indeed a natural approach, that almost all rates are log-linear, and at age 5...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A Bayesian Approach to Multistate Hidden Markov Models: Application to Dementia Progression

Williams

Storlie

Therneau

et al. 2019

Journal of the American Statistical Association

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“…A value of H = 10 was used to generate the results below and was deemed sufficient since all of the posterior samples had max i,t B i (t) < 10. As was done in Storlie, Michalak, Quinn, Dubois, Wender and Dubois (2013), the continuous functions B i ,z i,h and δ i,l are resolved on a fine time…”

Section: Prior Distributions and Computationmentioning

confidence: 99%

“…This process is restricted so that the average medication level for an entire round is consistent with the observed, average medication level for that round in the MEPS data. Latent variable approaches are common in the literature and have been used in similar situations (e.g., Muthen, 1983;Dunson, 2000;Storlie, Michalak, Quinn, Dubois, Wender and Dubois, 2013). Conditional on the latent medication process, the model for the multivariate response over time is a dependent log-Gaussian Cox Process (Møller, Syversveen and Waagepetersen, 1998;Brix and Diggle, 2001) with a mean function that depends on a patient's covariates and a random intercept.…”

Section: Introductionmentioning

confidence: 99%

Prediction of individual outcomes for asthma sufferers

et al. 2017

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We consider the problem of individual-specific medication level recommendation (initiation, removal, increase, or decrease) for asthma sufferers. Asthma is one of the most common chronic diseases in both adults and children, affecting 8% of the US population and costing $37-63 billion/year in the US. Asthma is a complex disease, whose symptoms may wax and wane, making it difficult for clinicians to predict outcomes and prognosis. Improved ability to predict prognosis can inform decision making and may promote conversations between clinician and provider around optimizing medication therapy. Data from the US Medical Expenditure Panel Survey (MEPS) years 2000-2010 were used to fit a longitudinal model for a multivariate response of adverse events (Emergency Department or In-patient visits, excessive rescue inhaler use, and oral steroid use). To reduce bias in the estimation of medication effects, medication level was treated as a latent process which was restricted to be consistent with prescription refill data. This approach is demonstrated to be effective in the MEPS cohort via predictions on a validation hold out set and a synthetic data simulation study. This framework can be easily generalized to medication decisions for other conditions as well.

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“…Such a scheme will be essential for HPC machines in the power-limited future. While sophisticated statistical models have been applied to reliability of HPC machines (Storlie et al 2013, Michalak et al 2012, to the best of our knowledge this is the first effort to statistically model the power process of HPC jobs. This paper also has online supplementary material containing Markov chain Monte Carlo (MCMC) estimation details.…”

Section: Overview Of the Statistical Approachmentioning

confidence: 99%

Modeling and Predicting Power Consumption of High Performance Computing Jobs

Storlie¹,

Sexton²,

Pakin³

et al. 2014

Preprint

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Power is becoming an increasingly important concern for large supercomputing centers. Due to cost concerns, data centers are becoming increasingly limited in their ability to enhance their power infrastructure to support increased compute power on the machineroom floor. At Los Alamos National Laboratory it is projected that future-generation supercomputers will be power-limited rather than budget-limited. That is, it will be less costly to acquire a large number of nodes than it will be to upgrade an existing datacenter and machine-room power infrastructure to run that large number of nodes at full power. In the power-limited systems of the future, machines will in principle be capable of drawing more power than they have available. Thus, power capping at the node/job level must be used to ensure the total system power draw remains below the available level. In this paper, we present a statistically grounded framework with which to predict (with uncertainty) how much power a given job will need and use these predictions to provide an optimal node-level power capping strategy. We model the power drawn by a given job (and subsequently by the entire machine) using hierarchical Bayesian modeling with hidden Markov and Dirichlet process models. We then demonstrate how this model can be used inside of a power-management scheme to minimize the affect of power capping on user jobs.

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A Bayesian Reliability Analysis of Neutron-Induced Errors in High Performance Computing Hardware

Cited by 8 publications

References 31 publications

A Bayesian Approach to Multistate Hidden Markov Models: Application to Dementia Progression

A Bayesian Approach to Multistate Hidden Markov Models: Application to Dementia Progression

Prediction of individual outcomes for asthma sufferers

Modeling and Predicting Power Consumption of High Performance Computing Jobs

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