Michael McLennan

HUBzero: A Platform for Dissemination and Collaboration in Computational Science and Engineering

The HUBzero cyberinfrastructure lets scientific researchers work together online to develop simulation and modeling tools. Other researchers can then access the resulting tools using an ordinary Web browser and launch simulation runs on the national Grid infrastructure, without having to download or compile any code.

nanoHUB.org: Advancing Education and Research in Nanotechnology

In 2002, the National Science Foundation established the Network for Computational Nanotechnology (NCN), a network of universities supporting the National Nanotechnology Initiative by bringing computational tools online, making the tools easy to use, and supporting the tools with educational materials. Along the way, NCN created a unique cyberinfrastructure to support its Web site, nanoHUB.org, where researchers, educators, and professionals collaborate, share resources, and solve real nanotechnology problems. In 2007, nanoHUB.org served more than 56,000 users from 172 countries. In this article, the authors share their experiences in developing this cyberinfrastructure and using it, particularly in an educational context.

Importance of space‐charge effects in resonant tunneling devices

The consideration of space charge in the analysis of resonant tunneling devices leads to a substantial modification of the current‐voltage relationship. The region of negative differential resistance (NDR) is shifted to a higher voltage, and broadened along the voltage axis. Moreover, the peak value of current prior to NDR is reduced, leading to a reduction in the predicted peak‐to‐valley ratio. An approach is presented to include space‐charge effects, and a recently fabricated GaAs‐AlxGa1−xAs structure is analyzed, to underscore the importance of a self‐consistent electrostatic potential in theoretical calculations.

Hub-based Simulation and Graphics Hardware Accelerated Visualization for Nanotechnology Applications

The Network for computational nanotechnology (NCN) has developed a science gateway at nanoHUB.org for nanotechnology education and research. Remote users can browse through online seminars and courses, and launch sophisticated nanotechnology simulation tools, all within their Web browser. Simulations are supported by a middleware that can route complex jobs to grid supercomputing resources. But what is truly unique about the middleware is the way that it uses hardware accelerated graphics to support both problem setup and result visualization. This paper describes the design and integration of a remote visualization framework into the nanoHUB for interactive visual analytics of nanotechnology simulations. Our services flexibly handle a variety of nanoscience simulations, render them utilizing graphics hardware acceleration in a scalable manner, and deliver them seamlessly through the middleware to the user. Rendering is done only on-demand, as needed, so each graphics hardware unit can simultaneously support many user sessions. Additionally, a novel node distribution scheme further improves our systems scalability. Our approach is not only efficient but also cost-effective. Only half-dozen render nodes are anticipated to support hundreds of active tool sessions on the nanoHUB. Moreover, this architecture and visual analytics environment provides capabilities that can serve many areas of scientific simulation and analysis beyond nanotechnology with its ability to interactively analyze and visualize multivariate scalar and vector fields

Quantum transport in ultrasmall electronic devices

Device analysis has traditionally been based on the semiclassical Boltzmann transport equation. Despite its impressive successes, this approach suffers from an important limitation; it cannot describe transport phenomena in which the wave nature of electrons plays a crucial role. A variety of such quantum effects have been discovered over the years, such as tunnelling, resonant tunnelling, weak and strong localisation, and the quantum Hall effect. Since 1985, experiments on ultrasmall structures (dimensions 100 nm) have revealed a number of new effects such as the Aharanov-Bohm effect, conductance fluctuations, non-local effects and the quantised resistance of point contacts. For ultrasmall structures at low temperature, these phenomena have clearly shown that electron transport is influenced by wave interference effects not unlike those well known in microwave networks. New device concepts are being proposed and demonstrated that are based on these wave properties. The authors review quantum interference effects that have been observed in ultrasmall structures, and their implications for future electronic devices. They also review the theoretical understanding of such phenomena and discuss some of the unresolved questions that have to be answered in order to develop accurate models for quantum device simulation.

An efficient method for the analysis of electron waveguides

Recent experiments in mesoscopic systems have demonstrated a striking similarity between electronic devices and optical or microwave waveguides. In the absence of impurities and phase‐breaking scattering, the geometry of such devices determines their behavior. These devices, which are usually composed of a network of guiding channels, can be analyzed in a manner similar to that used for electromagnetic waveguides. Sections of a device can be represented by scattering matrices, and individual scattering matrices can be combined, to determine the scattering matrix for an entire device. An efficient method is presented for the calculation of scattering matrices for devices with an arbitrary geometry. The method is applied to the analysis of a quantum reflection transistor, in which current is modulated between source and drain by a remote gate.

Science Gateways Institute Survey

Science gateways are digital interfaces to advanced technologies that support science/engineering research/education. Frequently implemented as Web and mobile applications, they provide access to community resources such as software, data, collaboration tools, instrumentation, and high‐performance computing. We anticipate opportunities for growth within a fragmented community. Through a large‐scale survey, we measured the extent and characteristics of the gateway community (reliance on gateways and nature of existing resources) to understand useful services and support for builders and users. We administered an online survey to nearly 29,000 principal investigators, senior administrators, and people with gateway affiliations. Nearly 5000 respondents represented diverse expertise and geography. The majority of researchers/educators indicated that specialized online resources were important to their work. They choose technologies by asking colleagues and looking for documentation, demonstrated reliability, and technical support; adaptability via customizing or open‐source standards was another priority. Research groups commonly provide their own resources, but public/academic institutions and commercial services also provide substantial offerings. Application creators and administrators welcome external services providing guidance such as technology selection, sustainability planning, evaluation, and specialized expertise (e.g., quality assurance and design). Technologies are diverse, so flexibility and ongoing community input are essential, as is offering specific, easy‐to‐access training, community support, and professional development. Copyright

Modeling and simulation of field-effect biosensors (BioFETs) and their deployment on the nanoHUB

BioFETs (biologically active field-effect transistors) are biosensors with a semiconductor transducer. Due to recent experiments demonstrating detection by a field effect, they have gained attention as potentially fast, reliable, and low-cost biosensors for a wide range of applications. Their advantages compared to other technologies are direct, label-free, ultrasensitive, and (near) real-time operation. We have developed 2D and 3D multi-scale models for planar sensor structures and for nanowire sensors. The multi-scale models are indispensable due to the large difference in the characteristic length scales of the biosensors: the charge distribution in the biofunctionalized surface layer varies on the Angstrom length scale, the diameters of the nanowires are several nanometers, and the sensor lengths measure several micrometers. The multi-scale models for the electrostatic potential can be coupled to any charge transport model of the transducer. Conductance simulations of nanowire sensors with different diameters provide numerical evidence for the importance of the dipole moment of the biofunctionalized surface layer in addition to its surface charge. We have also developed a web interface to our simulators, so that other researchers can access them at the nanohub and perform their own investigations.

HUBzero and Pegasus: integrating scientific workflows into science gateways

In this paper, we described the benefits and the challenges of integrating existing scientific workflow technologies into science gateways. Scientific workflow managers are powerful tools for handling large computational tasks. Domain scientists find it difficult to create new workflows, so many tasks that could benefit from workflow automation are often avoided or performed by hand. Two technologies have come together to bring the benefits of workflow to the masses. The Pegasus Workflow Management System can manage workflows comprised of millions of tasks, all the while recording data about the execution and intermediate results so that the provenance of the final result is clear. The HUBzero platform for scientific collaboration provides a venue for building and delivering tools to researchers and educators. With the press of a button, these tools can launch Pegasus workflows on national computing infrastructures and bring results back for plotting and visualization. As a result, the combination of Pegasus and HUBzero is bringing high‐throughput computing to a much wider audience. Copyright

PUQ: A code for non-intrusive uncertainty propagation in computer simulations

Abstract We present a software package for the non-intrusive propagation of uncertainties in input parameters through computer simulation codes or mathematical models and associated analysis; we demonstrate its use to drive micromechanical simulations using a phase field approach to dislocation dynamics. The PRISM uncertainty quantification framework (PUQ) offers several methods to sample the distribution of input variables and to obtain surrogate models (or response functions) that relate the uncertain inputs with the quantities of interest (QoIs); the surrogate models are ultimately used to propagate uncertainties. PUQ requires minimal changes in the simulation code, just those required to annotate the QoI(s) for its analysis. Collocation methods include Monte Carlo, Latin Hypercube and Smolyak sparse grids and surrogate models can be obtained in terms of radial basis functions and via generalized polynomial chaos. PUQ uses the method of elementary effects for sensitivity analysis in Smolyak runs. The code is available for download and also available for cloud computing in nanoHUB. PUQ orchestrates runs of the nanoPLASTICITY tool at nanoHUB where users can propagate uncertainties in dislocation dynamics simulations using simply a web browser, without downloading or installing any software. Program summary Program title: PUQ Catalogue identifier: AEWP_v1_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEWP_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: MIT license No. of lines in distributed program, including test data, etc.: 45075 No. of bytes in distributed program, including test data, etc.: 3318862 Distribution format: tar.gz Programming language: Python, C. Computer: Workstations. Operating system: Linux, Mac OSX. Classification: 4.11, 4.12, 4.13. External routines: SciPy, Matplotlib, h5py Nature of problem: Uncertainty propagation and creation of response surfaces. Solution method: Generalized Polynomial Chaos (gPC) using Smolyak sparse grids. Running time: PUQ performs uncertainty quantification and sensitivity analysis by running a simulation multiple times using different values for input parameters. Its run time will be the product of the run time of the chosen simulation code and the number of runs required to achieve the desired accuracy.