AiiDA: Automated Interactive Infrastructure and Database for Computational Science

Computational science has seen in the last decades a spectacular rise in the scope, breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still performed using the renaissance model of individual artisans gathered in a workshop, under the guidance of an established practitioner. Great benefits could follow instead from adopting concepts and tools coming from computer science to manage, preserve, and share these computational efforts. We illustrate here our paradigm sustaining such vision, based around the four pillars of Automation, Data, Environment, and Sharing. We then discuss its implementation in the open-source AiiDA platform (http://www.aiida.net), that has been tuned first to the demands of computational materials science. AiiDA's design is based on directed acyclic graphs to track the provenance of data and calculations, and ensure preservation and searchability. Remote computational resources are managed transparently, and automation is coupled with data storage to ensure reproducibility. Last, complex sequences of calculations can be encoded into scientific workflows. We believe that AiiDA's design and its sharing capabilities will encourage the creation of social ecosystems to disseminate codes, data, and scientific workflows.Comment: 30 pages, 7 figure

MOLNs: A cloud platform for interactive, reproducible and scalable spatial stochastic computational experiments in systems biology using PyURDME

Computational experiments using spatial stochastic simulations have led to important new biological insights, but they require specialized tools, a complex software stack, as well as large and scalable compute and data analysis resources due to the large computational cost associated with Monte Carlo computational workflows. The complexity of setting up and managing a large-scale distributed computation environment to support productive and reproducible modeling can be prohibitive for practitioners in systems biology. This results in a barrier to the adoption of spatial stochastic simulation tools, effectively limiting the type of biological questions addressed by quantitative modeling. In this paper, we present PyURDME, a new, user-friendly spatial modeling and simulation package, and MOLNs, a cloud computing appliance for distributed simulation of stochastic reaction-diffusion models. MOLNs is based on IPython and provides an interactive programming platform for development of sharable and reproducible distributed parallel computational experiments

AMP: A Science-driven Web-based Application for the TeraGrid

The Asteroseismic Modeling Portal (AMP) provides a web-based interface for astronomers to run and view simulations that derive the properties of Sun-like stars from observations of their pulsation frequencies. In this paper, we describe the architecture and implementation of AMP, highlighting the lightweight design principles and tools used to produce a functional fully-custom web-based science application in less than a year. Targeted as a TeraGrid science gateway, AMP's architecture and implementation are intended to simplify its orchestration of TeraGrid computational resources. AMP's web-based interface was developed as a traditional standalone database-backed web application using the Python-based Django web development framework, allowing us to leverage the Django framework's capabilities while cleanly separating the user interface development from the grid interface development. We have found this combination of tools flexible and effective for rapid gateway development and deployment.Comment: 7 pages, 2 figures, in Proceedings of the 5th Grid Computing Environments Worksho

WS-PGRADE/gUSE in European Projects

Besides core project partners, the SCI-BUS project also supported several external user communities in developing and setting up customized science gateways. The focus was on large communities typically represented by other European research projects. However, smaller local efforts with the potential of generalizing the solution to wider communities were also supported. This chapter gives an overview of support activities related to user communities external to the SCI-BUS project. A generic overview of such activities is provided followed by the detailed description of three gateways developed in collaboration with European projects: the agINFRA Science Gateway for Workflows for agricultural research, the VERCE Science Gateway for seismology, and the DRIHM Science Gateway for weather research and forecasting

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

Workflow Engineering in Materials Design within the BATTERY 2030+ Project

In recent years, modeling and simulation of materials have become indispensable to complement experiments in materials design. High-throughput simulations increasingly aid researchers in selecting the most promising materials for experimental studies or by providing insights inaccessible by experiment. However, this often requires multiple simulation tools to meet the modeling goal. As a result, methods and tools are needed to enable extensive-scale simulations with streamlined execution of all tasks within a complex simulation protocol, including the transfer and adaptation of data between calculations. These methods should allow rapid prototyping of new protocols and proper documentation of the process. Here an overview of the benefits and challenges of workflow engineering in virtual material design is presented. Furthermore, a selection of prominent scientific workflow frameworks used for the research in the BATTERY 2030+ project is presented. Their strengths and weaknesses as well as a selection of use cases in which workflow frameworks significantly contributed to the respective studies are discussed