The goal of the Data Assimilation Office is to produce accurate gridded datasets of atmospheric f... more The goal of the Data Assimilation Office is to produce accurate gridded datasets of atmospheric fields by assimilating a range of observations along with physi-cally consistent model forecasts. The DAO produces datasets that are used by the climate research community. ...
Steven Parker and Rob Armstrong and David Bernholdt and Tamara Dahlgren and Tom Epperly and Josep... more Steven Parker and Rob Armstrong and David Bernholdt and Tamara Dahlgren and Tom Epperly and Joseph Kenny and Manoj Krishnan and Gary Kumfert and Jay Larson and Lois Curfman McInnes and Jarek Nieplocha and Jaideep Ray and Sveta Shasharina. Enabling ...
Symbolic dynamics1 is the study of sequences of symbols belonging to a discrete set of elements, ... more Symbolic dynamics1 is the study of sequences of symbols belonging to a discrete set of elements, the most commmon example being a sequence of ones and zeroes. Often the set of symbols is derived from a timeseries of a continuous variable through the introduction of a partition function--a process called symbolization. Symbolic dynamics has been used widely in the physical sciences; a geophysical example being the application of C1 and C2 complexity2 to hourly precipitation station data3. The C1 and C2 complexities are computed by examining subsequences--or words--of fixed length L in the limit of large values of L. Recent advances in information theory have led to techniques focused on the growth rate of the Shannon entropy and its asymptotic behavior in the limit of long words--levels of entropy convergence4. The result is a set of measures one can use to quantify the amount of memory stored in the sequence, whether or not an observer is able to synchronize to the sequence, and with what confidence it may be predicted. These techniques may also be used to uncover periodic behavior in the sequence. We are currently applying complexity theory and levels of entropy convergence to gridpoint timeseries from the NCAR/NCEP 50-year reanalysis5. Topics to be discussed include: a brief introduction to symbolic dynamics; a description of the partition function/symbolization strategy; a discussion of C1 and C2 complexity and entropy convergence rates and their utility; and example applications of these techniques to NCAR/NCEP 50-reanalyses gridpoint timeseries, resulting in maps of C1 and C2 complexities and entropy convergence rates. Finally, we will discuss how these results may be used to validate climate models. 1{Hao, Bai-Lin, Elementary Symbolic Dynamics and Chaos in Dissipative Systems, Wold Scientific, Singapore (1989)} 2{d'Alessandro, G. and Politi, A., Phys. Rev. Lett., 64, 1609-1612 (1990).} 3{Elsner, J. and Tsonis, A., J. Atmos. Sci., 50, 400-405 (1993).} 4{Crutchfield, J. and Feldman, D., Chaos, {bf 13}, 25-54 (2003).} 5{Kalnay, E.~, Kanamitsu, M.~, Kistler, R.~, Collins, W.~, Deaven, D.~, Gandin, L.~, Iredell, M.~, Saha, S.~, White, G.~, Woolen, J.~, Zhu, Y.~, Chelliah, M.~, Ebisuzaki, W.~, Higgins, W.~, Janowiak, J.~, Mo, K.~C.~, Ropelewski, C.~, Wang, J.~, Leetmaa, A.~, Reynolds, R.~, Jenne, R.~, and Joseph, D.~, Bull. Amer. Met. Soc., 77, 437-471 (1996).}
This paper continues to develop a fault tolerant extension of the sparse grid combination techniq... more This paper continues to develop a fault tolerant extension of the sparse grid combination technique recently proposed in [B. Harding and M. Hegland, ANZIAM J., 54 (CTAC2012), pp. C394-C411]. The approach is novel for two reasons, first it provides several levels in which one can exploit parallelism leading towards massively parallel implementations, and second, it provides algorithm-based fault tolerance so that solutions can still be recovered if failures occur during computation. We present a generalisation of the combination technique from which the fault tolerant algorithm is a consequence. Using a model for the time between faults on each node of a high performance computer we provide bounds on the expected error for interpolation with this algorithm. Numerical experiments on the scalar advection PDE demonstrate that the algorithm is resilient to faults on a real application. It is observed that the trade-off of recovery time to decreased accuracy of the solution is suitably small. A comparison with traditional checkpoint-restart methods applied to the combination technique show that our approach is highly scalable with respect to the number of faults.
Computational science faces new challenges posed by multiphysics and multiscale, or more generall... more Computational science faces new challenges posed by multiphysics and multiscale, or more generally put, coupled models. These systems are composites formed from separate subsystem models that interact via data exchanges. These data dependencies pose a coupling problem, and on distributed-memory computers, a parallel coupling problem. This paper presents a definition of terms and a set of organising principles for the coupling and parallel coupling problems. It is meant as a first step towards creating a theory of coupled models. These principles are then employed in a case study of a coupled climate model and offer remarkable insight into its structure.
2015 International Conference on High Performance Computing & Simulation (HPCS), 2015
Applications performing ultra-large scale simulations via solving PDEs require very large computa... more Applications performing ultra-large scale simulations via solving PDEs require very large computational systems for their timely solution. Studies have shown the rate of failure grows with the system size and these trends are likely to worsen in future machines as less reliable components are used to reduce the energy cost. Thus, as systems, and the problems solved on them, continue to grow, the ability to survive failures is becoming a critical aspect of algorithm development. The sparse grid combination technique (SGCT) is a cost-effective method for solving time-evolving PDEs, especially for higherdimensional problems. It can also be easily modified to provide algorithm-based fault tolerance for these problems. In this paper, we show how the SGCT can produce a fault-tolerant version of the GENE gyrokinetic plasma application, which evolves a 5D complex density field over time. We use an alternate component grid combination formula to recover data from lost processes. User Level Failure Mitigation (ULFM) MPI is used to recover the processes, and our implementation is robust over multiple failures and recovery for both process and node failures. An acceptable degree of modification of the application is required. Results using the SGCT on two of the fields' dimensions show competitive execution times with acceptable error (within 0.1%), compared to the same simulation with a single full resolution grid. The benefits improve when the SGCT is used over three dimensions. Our experiments show that the GENE application can successfully recover from multiple process failures, and applying the SGCT the corresponding number of times minimizes the error for the lost sub-grids. Application recovery overhead via ULFM MPI increases from ∼1.5s at 64 cores to ∼5s at 2048 cores for a oneoff failure. This compares favourably to using GENE's in-built checkpointing with job restart in conjunction with the classical SGCT on failure, which have overheads four times as large for a single failure, excluding the backtrack overhead. An analysis for a long-running application taking into account checkpoint backtrack times indicates a reduction in overhead of over an order of magnitude.
Painlevé singularity analysis is applied to a simple model for charged particle motion in magneti... more Painlevé singularity analysis is applied to a simple model for charged particle motion in magnetic reconnection regions. We identify two field geometries for which test-particle motion is integrable; i.e. there exists a complete set ofisolating integrals: the neutral sheet configuration and the circular 0-line magnetic field. Both of these systems are solvable in terms of the second Painlevé transcendent. An infinite set ofweak Painlevé candidates is also discussed.
2005 IEEE International Conference on Cluster Computing, 2005
Commodity clusters have enabled ambitious multiphysics or coupled modeling of complex, mutually i... more Commodity clusters have enabled ambitious multiphysics or coupled modeling of complex, mutually interacting, computationally intensive systems in science and engineering. Each individual sub-system is represented as a component with its own parallel processor layout and requirements for temporal advance. A central challenge in developing such systems is the parallel coupling problem, which involves overall system architecture and the automation of component registration, distribution of the processor pool between individual components, parallel data transfer and transformation. There currently exist efficient mechanisms for automating parallel data transfer and transformation such as MCT and MPCCI. Mechanisms for top-level system integration, including component registration and resource allocation, scheduling, and control at runtime are less mature and face even greater challenges in heterogeneous environments. We will discuss the numerous architectural choices faced in framework and parallel coupled application development, and will illustrate them through a comparison of these mechanisms in four scientific application frameworks: the Community Climate System Model, the Space Weather Modeling Framework, the Earth System Modeling Framework, and the Weather Research and Forecasting Model. We will then discuss a more sophisticated set of requirements for automating these functions in application frameworks for heterogeneous clusters and computational grids.
Parallel implementation of the sparse grid combination technique in high dimensions presents many... more Parallel implementation of the sparse grid combination technique in high dimensions presents many complexity challenges. We enumerate these challenges, classifying them respectively as computational, algorithmic, and software complexity. We discuss strategies for overcoming the individual complexity barriers. We describe our architecture for a software framework that will allow its users to build complex multiple grid solver applications.
2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, 2013
ABSTRACT Computationally-demanding, parallel coupled models are crucial to understanding many imp... more ABSTRACT Computationally-demanding, parallel coupled models are crucial to understanding many important multi-physics/multiscale phenomena. Load-balancing such simulation son large clusters is often done through off-line, static means that often require significant manual input. Dynamic, runtime load-balancing has been shown in our previous work to be effective, but we still used a manually generated performance predictor to guide the load-balancing decisions. In this paper, we show how timing and interaction information obtained by instrumenting the middleware can be used to automatically generate a performance predictor that relates the overall execution time to the execution time of each individual sub model. The performance predictor is evaluated through the new coupled model benchmark employing five constituent sub models that simulates the CCSM coupled climate model.
We evaluate the equilibrium response of the atmospheric climate to a wide range of systematic var... more We evaluate the equilibrium response of the atmospheric climate to a wide range of systematic variations in carbon dioxide (100 to 1000 ppm) and solar luminosity (•.5% of today's value) using the NCAR CCM 1. The quantitative responses of each set of simulations are compared using a formal sensitivity analysis. Although the forcings on the climate are different, both temporally and spatially, similarities appear in the climatic response. The results of this analysis have broader implications for the use of simulations of past climates as a guide for possible future climatic change.
International Journal of High Performance Computing Applications, 2000
The Model Coupling Toolkit (MCT) is a software library for constructing parallel coupled models f... more The Model Coupling Toolkit (MCT) is a software library for constructing parallel coupled models from individual parallel models. MCT was created to address the challenges of creating a parallel coupler for the Community Climate System Model (CCSM). Each of the submodels that make up CCSM is a separate parallel application with its own domain decomposition, running on its own set of processors. This application contains multiple instances of the MxN problem, the problem of transferring data between two parallel programs running on disjoint sets of processors. CCSM also requires efficient data transfer to facilitate its interpolation algorithms. MCT was created as a generalized solution to handle these and other common functions in parallel coupled models. Here we describe MCT's implementation of the data transfer infrastructure needed for a parallel coupled model. The performance of MCT scales satisfactorily as processors are added to the system. However, the types of decompositions used in the submodels can affect performance. MCT's infrastructure provides a flexible and high-performing set of tools for enabling interoperability between parallel applications.
This article is a theoretical basis for the software implementation of the PhysicalspaceStatistic... more This article is a theoretical basis for the software implementation of the PhysicalspaceStatistical Analysis System (PSAS) that is used for atmospheric data analysis atthe NASA Data Assimilation Office (DAO). The PSAS implements a statistical algorithmthat combines irregularly spaced observations with a gridded forecast to producean optimal estimate of the state of the atmosphere. Starting from models for the forecastand observation
Abstract. Multiphysics and multiscale simulation systems are emerg-ing as a new grand challenge i... more Abstract. Multiphysics and multiscale simulation systems are emerg-ing as a new grand challenge in computational science, largely because of increased computing power provided by the distributed-memory par-allel programming model on commodity clusters. These systems ...
Lecture Notes in Computational Science and Engineering, 2006
Page 1. 10 Parallel PDE-Based Simulations Using the Common Component Architecture Lois Curfman Mc... more Page 1. 10 Parallel PDE-Based Simulations Using the Common Component Architecture Lois Curfman McInnes 1 , Benjamin A. Allan 2 , Robert Armstrong 2 , Steven J. Benson 1 , David E. Bernholdt 3 , Tamara ...
The FACETS (Framework Application for Core-Edge Transport Simulations) project began in January 2... more The FACETS (Framework Application for Core-Edge Transport Simulations) project began in January 2007 with the goal of providing core to wall transport modeling of a tokamak fusion reactor. This involves coupling previously separate computations for the core, edge, and wall regions. Such a coupling is primarily through connection regions of lower dimensionality. The project has started developing a component-based coupling framework to bring together models for each of these regions. In the first year, the core model will be a 1 ½ dimensional model (1D transport across flux surfaces coupled to a 2D equilibrium) with fixed equilibrium. The initial edge model will be the fluid model, UEDGE, but inclusion of kinetic models is planned for the out years. The project also has an embedded Scientific Application Partnership that is examining embedding a full-scale turbulence model for obtaining the crosssurface fluxes into a core transport code.
The goal of the Data Assimilation Office is to produce accurate gridded datasets of atmospheric f... more The goal of the Data Assimilation Office is to produce accurate gridded datasets of atmospheric fields by assimilating a range of observations along with physi-cally consistent model forecasts. The DAO produces datasets that are used by the climate research community. ...
Steven Parker and Rob Armstrong and David Bernholdt and Tamara Dahlgren and Tom Epperly and Josep... more Steven Parker and Rob Armstrong and David Bernholdt and Tamara Dahlgren and Tom Epperly and Joseph Kenny and Manoj Krishnan and Gary Kumfert and Jay Larson and Lois Curfman McInnes and Jarek Nieplocha and Jaideep Ray and Sveta Shasharina. Enabling ...
Symbolic dynamics1 is the study of sequences of symbols belonging to a discrete set of elements, ... more Symbolic dynamics1 is the study of sequences of symbols belonging to a discrete set of elements, the most commmon example being a sequence of ones and zeroes. Often the set of symbols is derived from a timeseries of a continuous variable through the introduction of a partition function--a process called symbolization. Symbolic dynamics has been used widely in the physical sciences; a geophysical example being the application of C1 and C2 complexity2 to hourly precipitation station data3. The C1 and C2 complexities are computed by examining subsequences--or words--of fixed length L in the limit of large values of L. Recent advances in information theory have led to techniques focused on the growth rate of the Shannon entropy and its asymptotic behavior in the limit of long words--levels of entropy convergence4. The result is a set of measures one can use to quantify the amount of memory stored in the sequence, whether or not an observer is able to synchronize to the sequence, and with what confidence it may be predicted. These techniques may also be used to uncover periodic behavior in the sequence. We are currently applying complexity theory and levels of entropy convergence to gridpoint timeseries from the NCAR/NCEP 50-year reanalysis5. Topics to be discussed include: a brief introduction to symbolic dynamics; a description of the partition function/symbolization strategy; a discussion of C1 and C2 complexity and entropy convergence rates and their utility; and example applications of these techniques to NCAR/NCEP 50-reanalyses gridpoint timeseries, resulting in maps of C1 and C2 complexities and entropy convergence rates. Finally, we will discuss how these results may be used to validate climate models. 1{Hao, Bai-Lin, Elementary Symbolic Dynamics and Chaos in Dissipative Systems, Wold Scientific, Singapore (1989)} 2{d'Alessandro, G. and Politi, A., Phys. Rev. Lett., 64, 1609-1612 (1990).} 3{Elsner, J. and Tsonis, A., J. Atmos. Sci., 50, 400-405 (1993).} 4{Crutchfield, J. and Feldman, D., Chaos, {bf 13}, 25-54 (2003).} 5{Kalnay, E.~, Kanamitsu, M.~, Kistler, R.~, Collins, W.~, Deaven, D.~, Gandin, L.~, Iredell, M.~, Saha, S.~, White, G.~, Woolen, J.~, Zhu, Y.~, Chelliah, M.~, Ebisuzaki, W.~, Higgins, W.~, Janowiak, J.~, Mo, K.~C.~, Ropelewski, C.~, Wang, J.~, Leetmaa, A.~, Reynolds, R.~, Jenne, R.~, and Joseph, D.~, Bull. Amer. Met. Soc., 77, 437-471 (1996).}
This paper continues to develop a fault tolerant extension of the sparse grid combination techniq... more This paper continues to develop a fault tolerant extension of the sparse grid combination technique recently proposed in [B. Harding and M. Hegland, ANZIAM J., 54 (CTAC2012), pp. C394-C411]. The approach is novel for two reasons, first it provides several levels in which one can exploit parallelism leading towards massively parallel implementations, and second, it provides algorithm-based fault tolerance so that solutions can still be recovered if failures occur during computation. We present a generalisation of the combination technique from which the fault tolerant algorithm is a consequence. Using a model for the time between faults on each node of a high performance computer we provide bounds on the expected error for interpolation with this algorithm. Numerical experiments on the scalar advection PDE demonstrate that the algorithm is resilient to faults on a real application. It is observed that the trade-off of recovery time to decreased accuracy of the solution is suitably small. A comparison with traditional checkpoint-restart methods applied to the combination technique show that our approach is highly scalable with respect to the number of faults.
Computational science faces new challenges posed by multiphysics and multiscale, or more generall... more Computational science faces new challenges posed by multiphysics and multiscale, or more generally put, coupled models. These systems are composites formed from separate subsystem models that interact via data exchanges. These data dependencies pose a coupling problem, and on distributed-memory computers, a parallel coupling problem. This paper presents a definition of terms and a set of organising principles for the coupling and parallel coupling problems. It is meant as a first step towards creating a theory of coupled models. These principles are then employed in a case study of a coupled climate model and offer remarkable insight into its structure.
2015 International Conference on High Performance Computing & Simulation (HPCS), 2015
Applications performing ultra-large scale simulations via solving PDEs require very large computa... more Applications performing ultra-large scale simulations via solving PDEs require very large computational systems for their timely solution. Studies have shown the rate of failure grows with the system size and these trends are likely to worsen in future machines as less reliable components are used to reduce the energy cost. Thus, as systems, and the problems solved on them, continue to grow, the ability to survive failures is becoming a critical aspect of algorithm development. The sparse grid combination technique (SGCT) is a cost-effective method for solving time-evolving PDEs, especially for higherdimensional problems. It can also be easily modified to provide algorithm-based fault tolerance for these problems. In this paper, we show how the SGCT can produce a fault-tolerant version of the GENE gyrokinetic plasma application, which evolves a 5D complex density field over time. We use an alternate component grid combination formula to recover data from lost processes. User Level Failure Mitigation (ULFM) MPI is used to recover the processes, and our implementation is robust over multiple failures and recovery for both process and node failures. An acceptable degree of modification of the application is required. Results using the SGCT on two of the fields' dimensions show competitive execution times with acceptable error (within 0.1%), compared to the same simulation with a single full resolution grid. The benefits improve when the SGCT is used over three dimensions. Our experiments show that the GENE application can successfully recover from multiple process failures, and applying the SGCT the corresponding number of times minimizes the error for the lost sub-grids. Application recovery overhead via ULFM MPI increases from ∼1.5s at 64 cores to ∼5s at 2048 cores for a oneoff failure. This compares favourably to using GENE's in-built checkpointing with job restart in conjunction with the classical SGCT on failure, which have overheads four times as large for a single failure, excluding the backtrack overhead. An analysis for a long-running application taking into account checkpoint backtrack times indicates a reduction in overhead of over an order of magnitude.
Painlevé singularity analysis is applied to a simple model for charged particle motion in magneti... more Painlevé singularity analysis is applied to a simple model for charged particle motion in magnetic reconnection regions. We identify two field geometries for which test-particle motion is integrable; i.e. there exists a complete set ofisolating integrals: the neutral sheet configuration and the circular 0-line magnetic field. Both of these systems are solvable in terms of the second Painlevé transcendent. An infinite set ofweak Painlevé candidates is also discussed.
2005 IEEE International Conference on Cluster Computing, 2005
Commodity clusters have enabled ambitious multiphysics or coupled modeling of complex, mutually i... more Commodity clusters have enabled ambitious multiphysics or coupled modeling of complex, mutually interacting, computationally intensive systems in science and engineering. Each individual sub-system is represented as a component with its own parallel processor layout and requirements for temporal advance. A central challenge in developing such systems is the parallel coupling problem, which involves overall system architecture and the automation of component registration, distribution of the processor pool between individual components, parallel data transfer and transformation. There currently exist efficient mechanisms for automating parallel data transfer and transformation such as MCT and MPCCI. Mechanisms for top-level system integration, including component registration and resource allocation, scheduling, and control at runtime are less mature and face even greater challenges in heterogeneous environments. We will discuss the numerous architectural choices faced in framework and parallel coupled application development, and will illustrate them through a comparison of these mechanisms in four scientific application frameworks: the Community Climate System Model, the Space Weather Modeling Framework, the Earth System Modeling Framework, and the Weather Research and Forecasting Model. We will then discuss a more sophisticated set of requirements for automating these functions in application frameworks for heterogeneous clusters and computational grids.
Parallel implementation of the sparse grid combination technique in high dimensions presents many... more Parallel implementation of the sparse grid combination technique in high dimensions presents many complexity challenges. We enumerate these challenges, classifying them respectively as computational, algorithmic, and software complexity. We discuss strategies for overcoming the individual complexity barriers. We describe our architecture for a software framework that will allow its users to build complex multiple grid solver applications.
2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing, 2013
ABSTRACT Computationally-demanding, parallel coupled models are crucial to understanding many imp... more ABSTRACT Computationally-demanding, parallel coupled models are crucial to understanding many important multi-physics/multiscale phenomena. Load-balancing such simulation son large clusters is often done through off-line, static means that often require significant manual input. Dynamic, runtime load-balancing has been shown in our previous work to be effective, but we still used a manually generated performance predictor to guide the load-balancing decisions. In this paper, we show how timing and interaction information obtained by instrumenting the middleware can be used to automatically generate a performance predictor that relates the overall execution time to the execution time of each individual sub model. The performance predictor is evaluated through the new coupled model benchmark employing five constituent sub models that simulates the CCSM coupled climate model.
We evaluate the equilibrium response of the atmospheric climate to a wide range of systematic var... more We evaluate the equilibrium response of the atmospheric climate to a wide range of systematic variations in carbon dioxide (100 to 1000 ppm) and solar luminosity (•.5% of today's value) using the NCAR CCM 1. The quantitative responses of each set of simulations are compared using a formal sensitivity analysis. Although the forcings on the climate are different, both temporally and spatially, similarities appear in the climatic response. The results of this analysis have broader implications for the use of simulations of past climates as a guide for possible future climatic change.
International Journal of High Performance Computing Applications, 2000
The Model Coupling Toolkit (MCT) is a software library for constructing parallel coupled models f... more The Model Coupling Toolkit (MCT) is a software library for constructing parallel coupled models from individual parallel models. MCT was created to address the challenges of creating a parallel coupler for the Community Climate System Model (CCSM). Each of the submodels that make up CCSM is a separate parallel application with its own domain decomposition, running on its own set of processors. This application contains multiple instances of the MxN problem, the problem of transferring data between two parallel programs running on disjoint sets of processors. CCSM also requires efficient data transfer to facilitate its interpolation algorithms. MCT was created as a generalized solution to handle these and other common functions in parallel coupled models. Here we describe MCT's implementation of the data transfer infrastructure needed for a parallel coupled model. The performance of MCT scales satisfactorily as processors are added to the system. However, the types of decompositions used in the submodels can affect performance. MCT's infrastructure provides a flexible and high-performing set of tools for enabling interoperability between parallel applications.
This article is a theoretical basis for the software implementation of the PhysicalspaceStatistic... more This article is a theoretical basis for the software implementation of the PhysicalspaceStatistical Analysis System (PSAS) that is used for atmospheric data analysis atthe NASA Data Assimilation Office (DAO). The PSAS implements a statistical algorithmthat combines irregularly spaced observations with a gridded forecast to producean optimal estimate of the state of the atmosphere. Starting from models for the forecastand observation
Abstract. Multiphysics and multiscale simulation systems are emerg-ing as a new grand challenge i... more Abstract. Multiphysics and multiscale simulation systems are emerg-ing as a new grand challenge in computational science, largely because of increased computing power provided by the distributed-memory par-allel programming model on commodity clusters. These systems ...
Lecture Notes in Computational Science and Engineering, 2006
Page 1. 10 Parallel PDE-Based Simulations Using the Common Component Architecture Lois Curfman Mc... more Page 1. 10 Parallel PDE-Based Simulations Using the Common Component Architecture Lois Curfman McInnes 1 , Benjamin A. Allan 2 , Robert Armstrong 2 , Steven J. Benson 1 , David E. Bernholdt 3 , Tamara ...
The FACETS (Framework Application for Core-Edge Transport Simulations) project began in January 2... more The FACETS (Framework Application for Core-Edge Transport Simulations) project began in January 2007 with the goal of providing core to wall transport modeling of a tokamak fusion reactor. This involves coupling previously separate computations for the core, edge, and wall regions. Such a coupling is primarily through connection regions of lower dimensionality. The project has started developing a component-based coupling framework to bring together models for each of these regions. In the first year, the core model will be a 1 ½ dimensional model (1D transport across flux surfaces coupled to a 2D equilibrium) with fixed equilibrium. The initial edge model will be the fluid model, UEDGE, but inclusion of kinetic models is planned for the out years. The project also has an embedded Scientific Application Partnership that is examining embedding a full-scale turbulence model for obtaining the crosssurface fluxes into a core transport code.
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Papers by Jay Larson