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

TIFF: Gyrofluid Turbulence in Full-f and Full-k

A model and code (TIFF) for isothermal gyrofluid computation of quasi-two-dimensional interchange and drift wave turbulence in magnetized plasmas with arbitrary fluctuation amplitudes (full-f) and arbitrary polarization wavelengths (full-k) is introduced. The model reduces to the paradigmatic Hasegawa-Wakatani model in the limits of small turbulence amplitudes (delta-f), cold ions (without finite Larmor radius effects), and homogeneous magnetic field. Several solvers are compared for the generalized Poisson problem, that is intrinsic to the full-f gyrofluid (and gyrokinetic) polarization equation, and a novel implementation based on a dynamically corrected Fourier method is proposed. The code serves as a reference case for further development of three-dimensional full-f full-k models and solvers, and for fundamental exploration of large amplitude turbulence in the edge of magnetized plasmas

sputniPIC: an Implicit Particle-in-Cell Code for Multi-GPU Systems

Large-scale simulations of plasmas are essential for advancing our understanding of fusion devices, space, and astrophysical systems. Particle-in-Cell (PIC) codes have demonstrated their success in simulating numerous plasma phenomena on HPC systems. Today, flagship supercomputers feature multiple GPUs per compute node to achieve unprecedented computing power at high power efficiency. PIC codes require new algorithm design and implementation for exploiting such accelerated platforms. In this work, we design and optimize a three-dimensional implicit PIC code, called sputniPIC, to run on a general multi-GPU compute node. We introduce a particle decomposition data layout, in contrast to domain decomposition on CPU-based implementations, to use particle batches for overlapping communication and computation on GPUs. sputniPIC also natively supports different precision representations to achieve speed up on hardware that supports reduced precision. We validate sputniPIC through the well-known GEM challenge and provide performance analysis. We test sputniPIC on three multi-GPU platforms and report a 200-800x performance improvement with respect to the sputniPIC CPU OpenMP version performance. We show that reduced precision could further improve performance by 45% to 80% on the three platforms. Because of these performance improvements, on a single node with multiple GPUs, sputniPIC enables large-scale three-dimensional PIC simulations that were only possible using clusters.Comment: Accepted for publication at the 32nd International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD 2020

Efficient multilevel scheduling in grids and clouds with dynamic provisioning

Tesis de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Arquitectura de Computadores y Automática, leída el 12-01-2016La consolidación de las grandes infraestructuras para la Computación Distribuida ha resultado en una plataforma de Computación de Alta Productividad que está lista para grandes cargas de trabajo. Los mejores exponentes de este proceso son las federaciones grid actuales. Por otro lado, la Computación Cloud promete ser más flexible, utilizable, disponible y simple que la Computación Grid, cubriendo además muchas más necesidades computacionales que las requeridas para llevar a cabo cálculos distribuidos. En cualquier caso, debido al dinamismo y la heterogeneidad presente en grids y clouds, encontrar la asignación ideal de las tareas computacionales en los recursos disponibles es, por definición un problema NP-completo, y sólo se pueden encontrar soluciones subóptimas para estos entornos. Sin embargo, la caracterización de estos recursos en ambos tipos de infraestructuras es deficitaria. Los sistemas de información disponibles no proporcionan datos fiables sobre el estado de los recursos, lo cual no permite la planificación avanzada que necesitan los diferentes tipos de aplicaciones distribuidas. Durante la última década esta cuestión no ha sido resuelta para la Computación Grid y las infraestructuras cloud establecidas recientemente presentan el mismo problema. En este marco, los planificadores (brokers) sólo pueden mejorar la productividad de las ejecuciones largas, pero no proporcionan ninguna estimación de su duración. La planificación compleja ha sido abordada tradicionalmente por otras herramientas como los gestores de flujos de trabajo, los auto-planificadores o los sistemas de gestión de producción pertenecientes a ciertas comunidades de investigación. Sin embargo, el bajo rendimiento obtenido con estos mecanismos de asignación anticipada (early-binding) es notorio. Además, la diversidad en los proveedores cloud, la falta de soporte de herramientas de planificación y de interfaces de programación estandarizadas para distribuir la carga de trabajo, dificultan la portabilidad masiva de aplicaciones legadas a los entornos cloud...The consolidation of large Distributed Computing infrastructures has resulted in a High-Throughput Computing platform that is ready for high loads, whose best proponents are the current grid federations. On the other hand, Cloud Computing promises to be more flexible, usable, available and simple than Grid Computing, covering also much more computational needs than the ones required to carry out distributed calculations. In any case, because of the dynamism and heterogeneity that are present in grids and clouds, calculating the best match between computational tasks and resources in an effectively characterised infrastructure is, by definition, an NP-complete problem, and only sub-optimal solutions (schedules) can be found for these environments. Nevertheless, the characterisation of the resources of both kinds of infrastructures is far from being achieved. The available information systems do not provide accurate data about the status of the resources that can allow the advanced scheduling required by the different needs of distributed applications. The issue was not solved during the last decade for grids and the cloud infrastructures recently established have the same problem. In this framework, brokers only can improve the throughput of very long calculations, but do not provide estimations of their duration. Complex scheduling was traditionally tackled by other tools such as workflow managers, self-schedulers and the production management systems of certain research communities. Nevertheless, the low performance achieved by these earlybinding methods is noticeable. Moreover, the diversity of cloud providers and mainly, their lack of standardised programming interfaces and brokering tools to distribute the workload, hinder the massive portability of legacy applications to cloud environments...Depto. de Arquitectura de Computadores y AutomáticaFac. de InformáticaTRUEsubmitte

Advances in Grid Computing

This book approaches the grid computing with a perspective on the latest achievements in the field, providing an insight into the current research trends and advances, and presenting a large range of innovative research papers. The topics covered in this book include resource and data management, grid architectures and development, and grid-enabled applications. New ideas employing heuristic methods from swarm intelligence or genetic algorithm and quantum encryption are considered in order to explain two main aspects of grid computing: resource management and data management. The book addresses also some aspects of grid computing that regard architecture and development, and includes a diverse range of applications for grid computing, including possible human grid computing system, simulation of the fusion reaction, ubiquitous healthcare service provisioning and complex water systems

GWpilot: Enabling multi-level scheduling in distributed infrastructures with GridWay and pilot jobs

Current systems based on pilot jobs are not exploiting all the scheduling advantages that the technique offers, or they lack compatibility or adaptability. To overcome the limitations or drawbacks in existing approaches, this study presents a different general-purpose pilot system, GWpilot. This system provides individual users or institutions with a more easy-to-use, easy-toinstall, scalable, extendable, flexible and adjustable framework to efficiently run legacy applications. The framework is based on the GridWay meta-scheduler and incorporates the powerful features of this system, such as standard interfaces, fair-share policies, ranking, migration, accounting and compatibility with diverse infrastructures. GWpilot goes beyond establishing simple network overlays to overcome the waiting times in remote queues or to improve the reliability in task production. It properly tackles the characterisation problem in current infrastructures, allowing users to arbitrarily incorporate customised monitoring of resources and their running applications into the system. This functionality allows the new framework to implement innovative scheduling algorithms that accomplish the computational needs of a wide range of calculations faster and more efficiently. The system can also be easily stacked under other software layers, such as self-schedulers. The advanced techniques included by default in the framework result in significant performance improvements even when very short tasks are scheduled

GPGPU application in fusion science

GPGPUs have firmly earned their reputation in HPC (High Performance Computing) as hardware for massively parallel computation. However their application in fusion science is quite marginal and not considered a mainstream approach to numerical problems. Computation advances have increased immensely over the last decade and continue to accelerate. GPGPU boards were always an alternative and exotic approach to problem solving and scientific programming, which was cultivated only by enthusiasts and specialized programmers. Today it is about 10 years, since the first fully programmable GPUs appeared on the market. And due to exponential growth in processing power over the years GPGPUs are not the alternative choice any more, but they became the main choice for big problem solving. Originally developed for and dominating in fields such as image and media processing, image rendering, video encoding/decoding, image scaling, stereo vision and pattern recognition GPGPUs are also becoming mainstream computation platforms in scientific fields such as signal processing, physics, finance and biology. This PhD contains solutions and approaches to two relevant problems for fusion and plasma science using GPGPU processing. First problem belongs to the realms of plasma and accelerator physics. I will present number of plasma simulations built on a PIC (Particle In Cell) method such as plasma sheath simulation, electron beam simulation, negative ion beam simulation and space charge compensation simulation. Second problem belongs to the realms of tomography and real-time control. I will present number of simulated tomographic plasma reconstructions of Fourier-Bessel type and their analysis all in real-time oriented approach, i.e. GPGPU based implementations are integrated into MARTe environment. MARTe is a framework for real-time application developed at JET (Joint European Torus) and used in several european fusion labs. These two sets of problems represent a complete spectrum of GPGPU operation capabilities. PIC based problems are large complex simulations operated as batch processes, which do not have a time constraint and operate on huge amounts of memory. While tomographic plasma reconstructions are online (realtime) processes, which have a strict latency/time constraints suggested by the time scales of real-time control and operate on relatively small amounts of memory. Such a variety of problems covers a very broad range of disciplines and fields of science: such as plasma physics, NBI (Neutral Beam Injector) physics, tokamak physics, parallel computing, iterative/direct matrix solvers, PIC method, tomography and so on. PhD thesis also includes an extended performance analysis of Nvidia GPU cards considering the applicability to the real-time control and real-time performance. In order to approach the aforementioned problems I as a PhD candidate had to gain knowledge in those relevant fields and build a vast range of practical skills such as: parallel/sequential CPU programming, GPU programming, MARTe programming, MatLab programming, IDL programming and Python programming

A Vlasov-Hybrid Code with Hermite Expansion of the Distribution Function for the Study of Low Growth Rate Instabilities

PhDWithin turbulence there are many phenomena which are currently unsolved. In the solar wind temperature anisotropies and low growth rates instability have a dominant role in de ning the turbulent behaviour of plasma. Due to the non linearity of the equations involved in the description of the physics of plasmas numerical simulations are a fundamental tool to study the dynamics of turbulent phenomena. In particular, hybrid codes are widely used in space plasma applications due to their ability to simulate large regions of volume maintaining some kinetic e ects. However, due to the sensitivity to the initial level of noise in the simulation, low growth rate instabilities are particularly di cult to simulate. Particle in Cell-hybrid simulations require too many particles to reduce the initial noise, while Vlasovhybrid simulations require too many grid points to fully discretize spatial and velocity phase spaces. We present here a Vlasov-hybrid algorithm and code implementation where the distribution function is expanded in series of Hermite functions. Thanks to the properties of these it is possible to project the Vlasov equation to nd an equation for each coe cient of the expansion. These coe cients are advanced in time using a Current Advance Method algorithm with splitting method for the Vlasov operator. The former is treated explicitly, while the latter is treated implicitly with a GMRES solver. The current is advanced with a temporal ODE derived taking moments of the Vlasov equation. A 1D3V code is implemented, tested and used to study low growth rate instabilities such as a proton cyclotron instability and a ion/ion right hand resonant instability with small relative velocity drift between beam and core populations. The results are compared with existing hybrid algorithms that we implemented. A 2D3V parallelized version of the code is implemented and described here. Initial results are presented and future improvements are discussed