Plasma Edge Kinetic-MHD Modeling in Tokamaks Using Kepler Workflow for Code Coupling, Data Management and Visualization

A new predictive computer simulation tool targeting the development of the H-mode pedestal at the plasma edge in tokamaks and the triggering and dynamics of edge localized modes (ELMs) is presented in this report. This tool brings together, in a coordinated and effective manner, several first-principles physics simulation codes, stability analysis packages, and data processing and visualization tools. A Kepler workflow is used in order to carry out an edge plasma simulation that loosely couples the kinetic code, XGC0, with an ideal MHD linear stability analysis code, ELITE, and an extended MHD initial value code such as M3D or NIMROD. XGC0 includes the neoclassical ion-electron-neutral dynamics needed to simulate pedestal growth near the separatrix. The Kepler workflow processes the XGC0 simulation results into simple images that can be selected and displayed via the Dashboard, a monitoring tool implemented in AJAX allowing the scientist to track computational resources, examine running and archived jobs, and view key physics data, all within a standard Web browser. The XGC0 simulation is monitored for the conditions needed to trigger an ELM crash by periodically assessing the edge plasma pressure and current density profiles using the ELITE code. If an ELM crash is triggered, the Kepler workflow launches the M3D code on a moderate-size Opteron cluster to simulate the nonlinear ELM crash and to compute the relaxation of plasma profiles after the crash. This process is monitored through periodic outputs of plasma fluid quantities that are automatically visualized with AVS/Express and may be displayed on the Dashboard. Finally, the Kepler workflow archives all data outputs and processed images using HPSS, as well as provenance information about the software and hardware used to create the simulation. The complete process of preparing, executing and monitoring a coupled-code simulation of the edge pressure pedestal buildup and the ELM cycle using the Kepler scientific workflow system is described in this paper

HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR

Leveraging Interactivity and MPI for Environmental Applications

This paper describes two different approaches to exploiting interactivity and MPI support available in the Interactive European Grid project.The first application is an air pollution simulation using Lagrangian trajectory model to simulate the spread of pollutant particles released into the atmosphere. The performance of the sequential implementation of the application was not satisfactory, therefore a parallelization was planned. The MPI programming model was used because of some previous experience with it and its support in the grid infrastructure to be used. Then the interactivity enabling the user to receive visualizations of simulation steps and to exercise control over the application running in the grid was added. The user interface for interacting with the application was implemented as a plug-in into the Migrating Desktop user interface client platform. The other application is an interactive workflow management system, which is a modification of a previously developed system for management of applications composed of web and grid services. It allows users to manage more complex jobs, composed of several program executions, in an interactive and comfortable manner. The system uses the interactive channel of the project to forward commands from a GUI to the on-site workflow manager, and to control the job during execution. This tool is able to visualize the inner workflow of the application. User has complete in-execution control over the job, can see its partial results, and can even alter it while it is running. This allows not only to accommodate the job workflow to the data it produces, extend or shorten it, but also to interactively debug and tune the job

Workflow models for heterogeneous distributed systems

The role of data in modern scientific workflows becomes more and more crucial. The unprecedented amount of data available in the digital era, combined with the recent advancements in Machine Learning and High-Performance Computing (HPC), let computers surpass human performances in a wide range of fields, such as Computer Vision, Natural Language Processing and Bioinformatics. However, a solid data management strategy becomes crucial for key aspects like performance optimisation, privacy preservation and security. Most modern programming paradigms for Big Data analysis adhere to the principle of data locality: moving computation closer to the data to remove transfer-related overheads and risks. Still, there are scenarios in which it is worth, or even unavoidable, to transfer data between different steps of a complex workflow. The contribution of this dissertation is twofold. First, it defines a novel methodology for distributed modular applications, allowing topology-aware scheduling and data management while separating business logic, data dependencies, parallel patterns and execution environments. In addition, it introduces computational notebooks as a high-level and user-friendly interface to this new kind of workflow, aiming to flatten the learning curve and improve the adoption of such methodology. Each of these contributions is accompanied by a full-fledged, Open Source implementation, which has been used for evaluation purposes and allows the interested reader to experience the related methodology first-hand. The validity of the proposed approaches has been demonstrated on a total of five real scientific applications in the domains of Deep Learning, Bioinformatics and Molecular Dynamics Simulation, executing them on large-scale mixed cloud-High-Performance Computing (HPC) infrastructures

Couplers for linking environmental models: scoping study and potential next steps

This report scopes out what couplers there are available in the hydrology and atmospheric modelling fields. The work reported here examines both dynamic runtime and one way file based coupling. Based on a review of the peer-reviewed literature and other open sources, there are a plethora of coupling technologies and standards relating to file formats. The available approaches have been evaluated against criteria developed as part of the DREAM project. Based on these investigations, the following recommendations are made: • The most promising dynamic coupling technologies for use within BGS are OpenMI 2.0 and CSDMS (either 1.0 or 2.0) • Investigate the use of workflow engines: Trident and Pyxis, the latter as part of the TSB/AHRC project “Confluence” • There is a need to include database standards CSW and GDAL and use data formats from the climate community NetCDF and CF standards. • Development of a “standard” composition which will consist of two process models and a 3D geological model all linked to data stored in the BGS corporate database and flat file format. Web Feature Services should be included in these compositions. There is also a need to investigate other approaches in different disciplines: The Loss Modelling Framework, OASIS-LMF is the best candidate

Parallel Anisotropic Unstructured Grid Adaptation

Computational fluid dynamics (CFD) has become critical to the design and analysis of aerospace vehicles. Parallel grid adaptation that resolves multiple scales with anisotropy is identified as one of the challenges in the CFD Vision 2030 Study to increase the capacity and capability of CFD simulation. The study also cautions that computer architectures are undergoing a radical change, and dramatic increases in algorithm concurrency will be required to exploit full performance. This paper reviews four different methods to parallel anisotropic grid adaptation. They cover both ends of the spectrum: 1) using existing state-of-the-art software optimized for a single core and modifying it for parallel platforms, and 2) designing and implementing scalable software with incomplete but rapidly maturing functionality. A brief overview for each grid adaptation system is presented in the context of a telescopic approach for multilevel concurrency. These methods employ different approaches to enable parallel execution, which provides a unique opportunity to illustrate the relative behavior of each approach. Qualitative and quantitative metric evaluations are used to draw lessons for future developments in this critical area for parallel CFD simulation

Generic Metadata Handling in Scientific Data Life Cycles

Scientific data life cycles define how data is created, handled, accessed, and analyzed by users. Such data life cycles become increasingly sophisticated as the sciences they deal with become more and more demanding and complex with the coming advent of exascale data and computing. The overarching data life cycle management background includes multiple abstraction categories with data sources, data and metadata management, computing and workflow management, security, data sinks, and methods on how to enable utilization. Challenges in this context are manifold. One is to hide the complexity from the user and to enable seamlessness in using resources to usability and efficiency. Another one is to enable generic metadata management that is not restricted to one use case but can be adapted with limited effort to further ones. Metadata management is essential to enable scientists to save time by avoiding the need for manually keeping track of data, meaning for example by its content and location. As the number of files grows into the millions, managing data without metadata becomes increasingly difficult. Thus, the solution is to employ metadata management to enable the organization of data based on information about it. Previously, use cases tended to only support highly specific or no metadata management at all. Now, a generic metadata management concept is available that can be used to efficiently integrate metadata capabilities with use cases. The concept was implemented within the MoSGrid data life cycle that enables molecular simulations on distributed HPC-enabled data and computing infrastructures. The implementation enables easy-to-use and effective metadata management. Automated extraction, annotation, and indexing of metadata was designed, developed, integrated, and search capabilities provided via a seamless user interface. Further analysis runs can be directly started based on search results. A complete evaluation of the concept both in general and along the example implementation is presented. In conclusion, generic metadata management concept advances the state of the art in scientific date life cycle management