Browsing by Author "Burns, Patrick J., committee member"
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Item Open Access Constraints on the galactic magnetic field with two-point cumulative autocorrelation function(Colorado State University. Libraries, 2012) Petrov, Yevgeniy, author; Harton, John L., advisor; Mostafá, Miguel A., committee member; Berger, Bruce, committee member; Burns, Patrick J., committee memberThe fact that ultra high energy cosmic rays are charged particles complicates identication of their sources due to deflections by the intervening cosmic magnetic fields. The information about the fields is encoded in the amount of deflection experienced by a charged particle. Unfortunately, the positions of sources are unknown as is the structure of the magnetic field. However, it is possible to deduce the most favorable galactic magnetic field by examining the parameter space of different models of the galactic magnetic field. The method presented in this work is valid under some plausible assumptions, such as extragalactic origin of the UHECR, pure protonic composition above 50 EeV and sufficiently weak randomly oriented galactic and extragalactic components of the magnetic field. I use a two point cumulative autocorrelation function combined with the backtracking method to find regions in the parameter space that are compatible with statistically significant clustering on the extragalactic sky. This approach is independent of any catalog of sources. The ratio between the number of pairs within a certain angular window at the Earth sky and at the extragalactic sky after backtracking serves to indicate focusing or de-focusing properties of a particular field configuration. The results suggest that among several tested fields, the Harari-Mollerach-Roulet model with a bi-symmetric spiral and even vertical symmetry favors clustering of arrival directions at the extragalactic sky with the probability of 2.5% being from an isotropic distribution. Addition of the toroidal halo field improves clustering for the Harari-Mollerach-Roulet field for both bi-symmetric and axisymmetric spirals with even vertical symmetry, and the isotropic probabilities are 2.5% and 5.3% correspondingly. The bi-symmetric and axisymmetric spirals with odd vertical symmetry are disfavored, as well as the models with annular structure.Item Open Access Distributed systems in small scale research environments: Hadoop and the EM algorithm(Colorado State University. Libraries, 2011) Remington, Jason Michael, author; Draper, Bruce A. (Bruce Austin), 1962-, advisor; Böhm, Wim, advisor; Burns, Patrick J., committee memberDistributed systems are widely used in large scale high performance computing environments, and often conjure visions of enormous data centers full of thousands of networked machines working together. Smaller research environments may not have access to such a data center, and many jobs in these environments may still take weeks or longer to complete. Systems that work well on hundreds or thousands of machines on Terabyte and larger data sets may not scale down to small environments with a couple dozen machines and gigabyte data sets. This research determines the viability of one such system in a small research environment in order to determine what issues arise when scaling down to such a small environment. Specifically, we use Hadoop to implement the Expectation Maximization algorithm, which is iterative, stateful, inherently parallel, and computationally expensive. We find that the lack of support for modeling data dependencies between records results in large amounts of network traffic, and that the lack of support for iterative Map/Reduce magnifies the overhead on jobs which require multiple iterations. These results expose key issues which need to be addressed for the distributed system to perform well in a small research environment.Item Open Access Integration of variable photosynthetic capacity into a biogeochemical model(Colorado State University. Libraries, 2011) Straube, Jonathan R., author; Ojima, Dennis S., advisor; Parton, William J., committee member; Burns, Patrick J., committee memberWe integrated a photosynthetic sub-model into the daily Century model, DayCent, to improve the estimations of carbon fluxes at the Niwot Ridge LTER site; the new version is called DayCent-Photosyn. The photosynthetic sub-model, adapted from the SIPNET/PnET family of models, includes solar radiation and vapor pressure deficit controls on production, as well as temperature and water stress terms. A key feature we added to the base photosynthetic equations is the addition of a variable maximum net photosynthetic rate (Amax). We optimized the parameters controlling photosynthesis using a variation of the Metropolis-Hastings algorithm along with data-assimilation techniques. The model was optimized and validated against level 4 data available from the Ameriflux website using observed net ecosystem exchange (NEE) and estimated gross primary production (GPP) and ecosystem respiration (Re) values. The inclusion of a variable Amax rate greatly improved model performance (NEE RMSE = 0.63 gC m-2, AIC= 2099) versus a version with a single Amax parameter (NEE RMSE = 0.74 gC m-2, AIC= 3724). DayCent-Photosyn is able to capture the inter-annual and seasonal flux patterns, including the critical early season assimilation, but tends to overestimate yearly NEE uptake. The simulated influence of a variable Amax rate suggest a need for further studies on the process controls affecting the seasonal photosynthetic rates.Item Open Access Minimizing energy costs for geographically distributed heterogeneous data centers(Colorado State University. Libraries, 2018) Hogade, Ninad, author; Pasricha, Sudeep, advisor; Siegel, Howard Jay, advisor; Burns, Patrick J., committee memberThe recent proliferation and associated high electricity costs of distributed data centers have motivated researchers to study energy-cost minimization at the geo-distributed level. The development of time-of-use (TOU) electricity pricing models and renewable energy source models has provided the means for researchers to reduce these high energy costs through intelligent geographical workload distribution. However, neglecting important considerations such as data center cooling power, interference effects from task co-location in servers, net-metering, and peak demand pricing of electricity has led to sub-optimal results in prior work because these factors have a significant impact on energy costs and performance. In this thesis, we propose a set of workload management techniques that take a holistic approach to the energy minimization problem for geo-distributed data centers. Our approach considers detailed data center cooling power, co-location interference, TOU electricity pricing, renewable energy, net metering, and peak demand pricing distribution models. We demonstrate the value of utilizing such information by comparing against geo-distributed workload management techniques that possess varying amounts of system information. Our simulation results indicate that our best proposed technique is able to achieve a 61% (on average) cost reduction compared to state-of-the-art prior work.Item Open Access Optical performance of cylindrical absorber collectors with and without reflectors(Colorado State University. Libraries, 1994) Menon, Arun B., author; Duff, William, advisor; Burns, Patrick J., committee member; Zachmann, David W., committee memberThe optical efficiency of a solar collector, which depends on the collector geometry and material properties (i.e., geometry and radiative properties of the cover, absorber and any reflector), contributes significantly towards its overall performance. This optical efficiency is directly proportional to the transmittance-absorptance or τα product for all possible angles of incidence. A 3-D Monte Carlo ray tracing technique is used to determine this τα product for evacuated tubular collectors (ETCs) with cylindrical absorbers in an effort to identify the most efficient optical design parameters. These collectors are asymmetric with respect to the incident solar radiation and their optical efficiencies are therefore difficult to estimate using any other method. The collector geometry is modeled using constructive solid geometry (CSG). CSG allows the generation of complex collector shapes by combining simple primitive objects. The ray tracing algorithm tracks individual photons through the collector geometry to provide a means of obtaining the absorbed fraction for a particular angle of radiation incident on the collector plane. Incidence angle modifiers (IAMs), the ratio of the τα product at a particular set of longitudinal and transverse radiation incidence angles to the τα product at normal incidence are thereby obtained. IAMs are calculated for variations in five different design parameters to determine the most advantageous geometries. It is found that diffusely reflecting back planes significantly enhance optical performance of tubular collectors. Verification of the ray trace calculations is made by comparing with experimental results from the indoor solar simulator at CSU. TRNSYS predicted values of τα are within 1% of the ray trace results for normal incidence tests and within 7% for off-normal tests. Inaccuracies resulting from the use of a multiplicative technique wherein off-axis IAMs are obtained by a multiplicative combination of the biaxial IAMs are also addressed. The multiplicative approach is found to be very inaccurate for angles of incidence greater than 40°. To further assess the relative advantages of tubular collectors over flat plate collectors and whether a reflective back plane is really necessary, the two types of collectors are modeled in a simple fashion and the amount of radiation that is available for collection by each is determined. Calculations show that reflectors would probably not be required for collector slopes in excess of 50°. However, for slope angles less than 50°, a reflector placed behind the tubes is beneficial.Item Open Access Performance and reliability evaluation of Sacramento demonstration novel ICPC solar collectors(Colorado State University. Libraries, 2012) Daosukho, Jirachote "Pong", author; Duff, William S., advisor; Troxell, Wade O., advisor; Burns, Patrick J., committee member; Breidt, F. Jay, committee memberThis dissertation focuses on the reliability and degradation of the novel integral compound parabolic concentrator (ICPC) evacuated solar collector over a 13 year period. The study investigates failure modes of the collectors and analyzes the effects of those failures on performance. An instantaneous efficiency model was used to calculate performance and efficiencies from the measurements. An animated graphical ray tracing simulation tool was developed to investigate the optical performance of the ICPC for the vertical and horizontal absorber fin orientations. The animated graphical ray tracing allows the user to visualize the propagation of rays through the ICPC optics. The ray tracing analysis also showed that the horizontal fin ICPC's performance was more robust to degradation of the reflective surface. Thermal losses were also a part of the performance calculations. The two main degradation mechanisms are reflectivity degradation due to air leakage and fluid leakage into the vacuum enclosure and loss of vacuum due to leaks through cracks. Reflectivity degradation causes a reduction of optical performance and the loss of vacuum causes a reduction in thermal performance.Item Open Access Resource management for extreme scale high performance computing systems in the presence of failures(Colorado State University. Libraries, 2018) Dauwe, Daniel, author; Pasricha, Sudeep, advisor; Siegel, H. J., advisor; Maciejewski, Anthony A., committee member; Burns, Patrick J., committee memberHigh performance computing (HPC) systems, such as data centers and supercomputers, coordinate the execution of large-scale computation of applications over tens or hundreds of thousands of multicore processors. Unfortunately, as the size of HPC systems continues to grow towards exascale complexities, these systems experience an exponential growth in the number of failures occurring in the system. These failures reduce performance and increase energy use, reducing the efficiency and effectiveness of emerging extreme-scale HPC systems. Applications executing in parallel on individual multicore processors also suffer from decreased performance and increased energy use as a result of applications being forced to share resources, in particular, the contention from multiple application threads sharing the last-level cache causes performance degradation. These challenges make it increasingly important to characterize and optimize the performance and behavior of applications that execute in these systems. To address these challenges, in this dissertation we propose a framework for intelligently characterizing and managing extreme-scale HPC system resources. We devise various techniques to mitigate the negative effects of failures and resource contention in HPC systems. In particular, we develop new HPC resource management techniques for intelligently utilizing system resources through the (a) optimal scheduling of applications to HPC nodes and (b) the optimal configuration of fault resilience protocols. These resource management techniques employ information obtained from historical analysis as well as theoretical and machine learning methods for predictions. We use these data to characterize system performance, energy use, and application behavior when operating under the uncertainty of performance degradation from both system failures and resource contention. We investigate how to better characterize and model the negative effects from system failures as well as application co-location on large-scale HPC computing systems. Our analysis of application and system behavior also investigates: the interrelated effects of network usage of applications and fault resilience protocols; checkpoint interval selection and its sensitivity to system parameters for various checkpoint-based fault resilience protocols; and performance comparisons of various promising strategies for fault resilience in exascale-sized systems.Item Open Access Resource management for heterogeneous computing systems: utility maximization, energy-aware scheduling, and multi-objective optimization(Colorado State University. Libraries, 2015) Friese, Ryan, author; Siegel, Howard J., advisor; Maciejewski, Anthony A., advisor; Pasricha, Sudeep, committee member; Koenig, Gregory A., committee member; Burns, Patrick J., committee memberAs high performance heterogeneous computing systems continually become faster, the operating cost to run these systems has increased. A significant portion of the operating costs can be attributed to the amount of energy required for these systems to operate. To reduce these costs it is important for system administrators to operate these systems in an energy efficient manner. Additionally, it is important to be able to measure the performance of a given system so that the impacts of operating at different levels of energy efficiency can be analyzed. The goal of this research is to examine how energy and system performance interact with each other for a variety of environments. One part of this study considers a computing system and its corresponding workload based on the expectations for future environments of Department of Energy and Department of Defense interest. Numerous Heuristics are presented that maximize a performance metric created using utility functions. Additional heuristics and energy filtering techniques have been designed for a computing system that has the goal of maximizing the total utility earned while being subject to an energy constraint. A framework has been established to analyze the trade-offs between performance (utility earned) and energy consumption. Stochastic models are used to create "fuzzy" Pareto fronts to analyze the variability of solutions along the Pareto front when uncertainties in execution time and power consumption are present within a system. In addition to using utility earned as a measure of system performance, system makespan has also been studied. Finally, a framework has been developed that enables the investigation of the effects of P-states and memory interference on energy consumption and system performance.Item Open Access Resource management in heterogeneous computing systems with tasks of varying importance(Colorado State University. Libraries, 2014) Khemka, Bhavesh, author; Maciejewski, Anthony A., advisor; Siegel, H. J., advisor; Pasricha, Sudeep, committee member; Koenig, Gregory A., committee member; Burns, Patrick J., committee memberThe problem of efficiently assigning tasks to machines in heterogeneous computing environments where different tasks can have different levels of importance (or value) to the computing system is a challenging one. The goal of this work is to study this problem in a variety of environments. One part of the study considers a computing system and its corresponding workload based on the expectations for future environments of Department of Energy and Department of Defense interest. We design heuristics to maximize a performance metric created using utility functions. We also create a framework to analyze the trade-offs between performance and energy consumption. We design techniques to maximize performance in a dynamic environment that has a constraint on the energy consumption. Another part of the study explores environments that have uncertainty in the availability of the compute resources. For this part, we design heuristics and compare their performance in different types of environments.Item Open Access The future of networking is the future of big data(Colorado State University. Libraries, 2019) Shannigrahi, Susmit, author; Papadopoulos, Christos, advisor; Partridge, Craig, advisor; Pallickara, Shrideep, committee member; Ray, Indrakshi, committee member; Burns, Patrick J., committee member; Monga, Inder, committee memberScientific domains such as Climate Science, High Energy Particle Physics (HEP), Genomics, Biology, and many others are increasingly moving towards data-oriented workflows where each of these communities generates, stores and uses massive datasets that reach into terabytes and petabytes, and projected soon to reach exabytes. These communities are also increasingly moving towards a global collaborative model where scientists routinely exchange a significant amount of data. The sheer volume of data and associated complexities associated with maintaining, transferring, and using them, continue to push the limits of the current technologies in multiple dimensions - storage, analysis, networking, and security. This thesis tackles the networking aspect of big-data science. Networking is the glue that binds all the components of modern scientific workflows, and these communities are becoming increasingly dependent on high-speed, highly reliable networks. The network, as the common layer across big-science communities, provides an ideal place for implementing common services. Big-science applications also need to work closely with the network to ensure optimal usage of resources, intelligent routing of requests, and data. Finally, as more communities move towards data-intensive, connected workflows - adopting a service model where the network provides some of the common services reduces not only application complexity but also the necessity of duplicate implementations. Named Data Networking (NDN) is a new network architecture whose service model aligns better with the needs of these data-oriented applications. NDN's name based paradigm makes it easier to provide intelligent features at the network layer rather than at the application layer. This thesis shows that NDN can push several standard features to the network. This work is the first attempt to apply NDN in the context of large scientific data; in the process, this thesis touches upon scientific data naming, name discovery, real-world deployment of NDN for scientific data, feasibility studies, and the designs of in-network protocols for big-data science.