Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.

BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).

Developing software tools for science has always been a central vision of the SCI Institute.

Scientific Computing

Numerical simulation of real-world phenomena provides fertile ground for building interdisciplinary relationships. The SCI Institute has a long tradition of building these relationships in a win-win fashion – a win for the theoretical and algorithmic development of numerical modeling and simulation techniques and a win for the discipline-specific science of interest. High-order and adaptive methods, uncertainty quantification, complexity analysis, and parallelization are just some of the topics being investigated by SCI faculty. These areas of computing are being applied to a wide variety of engineering applications ranging from fluid mechanics and solid mechanics to bioelectricity.

Martin Berzins

Parallel Computing
GPUs

Mike Kirby

Finite Element Methods
Uncertainty Quantification
GPUs

Valerio Pascucci

Scientific Data Management

Chris Johnson

Problem Solving Environments

Ross Whitaker

GPUs

Chuck Hansen

GPUs

Amir Arzani

Scientific machine learning
Data-driven fluid flow modeling

Funded Research Projects:

Optimal Approximation Algorithms in High Dimensions

Akil Narayan
The increasing power of modern computational hardware has enabled computer-based simulation of sophisticated mathematical models that resolve important physical phenomena in great detail. With the advent of these computational abilities has come an increased demand to include more complex physical interactions in the models, and thus an increased strain on computational resources. Modern engineering design utilizes such models, and these design problems typically involve (1) numerous tunable parameters that affect reliability, cost, and failure, (2) uncertainty about external influences manifesting as randomness in the model, and (3) epistemic ignorance involving model form uncertainty. In realistic applications, the collection of these effects leads to predictions that depend on a cumulatively high-dimensional parameter. This project focuses on development and deployment of novel, near-optimal experimental design and sampling algorithms for the accurate and efficient simulation of physical models parameterized by high-dimensional inputs. The work of this project involves the application of recently developed approximation theory results in the computational arena, targeted advances that extend theoretical mathematics for computational purposes, and the development and implementation of algorithms for large-scale computations.

The technical aspects of this project are designed to provide feasible computational algorithms and concrete mathematical guarantees for tasks in high-dimensional approximation. The three major core components for the completion of this task involve the design, implementation, and analysis of algorithms that leverage optimality characteristics of (1) random and deterministic experimental and sampling design, (2) computational algorithms for identifying efficient sampling schemes, and (3) strategies and techniques for emerging approximation paradigms such as sparse approximation and dimension reduction. A crosscutting theme is application of these methods to problems of modern interest in scientific computing. This project involves fundamental contributions to the fields of applied approximation theory and computational approximation methods through the development of applications-oriented sampling designs with provable near-optimality. Theoretical investigations of this project connect classical techniques in approximation and linear algebra with emerging algorithms in data reduction and reduced order modeling. The implementation of these algorithms will significantly enhance theoretical understanding and computational feasibility for goal-oriented design, parameter study and reduction, sparse and compressive representations, model verification and calibration, and data-driven simulations.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Cyberinfrastructure Center of Excellence Pilot Study

Ewa Deelman, Valerio Pascucci, Anirban Mandal, Jaroslaw Nabrzyski, Robert Ricci
University of Southern California, Los Angeles, CA, United States

NSF's major multi-user research facilities (large facilities) are sophisticated research instruments and platforms - such as large telescopes, interferometers and distributed sensor arrays - that serve diverse scientific disciplines from astronomy and physics to geoscience and biological science. Large facilities are increasingly dependent on advanced cyberinfrastructure (CI) - computing, data and software systems, networking, and associated human capital - to enable broad delivery and analysis of facility-generated data. As a result of these cyber infrastructure tools, scientists and the public gain new insights into fundamental questions about the structure and history of the universe, the world we live in today, and how our plants and animals may change in the coming decades. The goal of this pilot project is to develop a model for a Cyberinfrastructure Center of Excellence (CI CoE) that facilitates community building and sharing and applies knowledge of best practices and innovative solutions for facility CI.

The pilot project will explore how such a center would facilitate CI improvements for existing facilities and for the design of new facilities that exploit advanced CI architecture designs and leverage establish tools and solutions. The pilot project will also catalyze a key function of an eventual CI CoE - to provide a forum for exchange of experience and knowledge among CI experts. The project will also gather best practices for large facilities, with the aim of enhancing individual facility CI efforts in the broader CI context. The discussion forum and planning effort for a future CI CoE will also address training and workforce development by expanding the pool of skilled facility CI experts and forging career paths for CI professionals. The result of this work will be a strategic plan for a CI CoE that will be evaluated and refined through community interactions: workshops and direct engagement with the facilities and the broader CI community.

This project is being supported by the Office of Advanced Cyberinfrastructure in the Directorate for Computer and Information Science and Engineering and the Division of Emerging Frontiers in the Directorate for Biological Sciences.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Efficiency and Productivity through Artificial Intelligence

Valerio Pascucci
Efficient cyberinfrastructure (advanced computing, data, software and networking infrastructure) is a critical component of the support that NSF provides for new discoveries in science and engineering. Cyberinfrastructure is complex and traditionally requires years of human hand-tuning to fully achieve maximal performance for scientific users. We propose to introduce Artificial Intelligence (AI) as a way to automatically and quickly optimize the performance and broadest use of recent NSF-supported advanced computing resources. Through this pilot effort our ultimate aim is to enable and accelerate scientific advances in widely diverse fields such as biology, chemistry, oceanography, materials science, climate modeling, and cosmology.

As the research cyberinfrastructure grows rapidly in scale and complexity, it is essential to integrate new technologies based on Machine Learning (ML) and AI to ensure that the investments in new hardware and software components result in proportional improvements in performance and capability. This project will undertake a transformative research activity targeting: (1) scaling ML algorithms to make them easily available to the scientific community; and (2) improving cyberinfrastructure efficiency through AI-based predictive models. This technical work will be complemented and informed by a community engagement effort to jointly catalog the state of the art and identify future challenges and opportunities in enabling a new smart cyberinfrastructure.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Robust and Scalable Multi-Fidelity Algorithms for Model-Based Predictions

Akil Narayan
Modern computational models are complex in nature: accurate predictions of physics require detailed and intensive computational resources. As such, development of accurate scientific models has been the area of research emphasis in recent decades. Today’s scientific models involve largescale simulation tools, often with many interdependent components, and sometimes requiring days to complete a single simulation. Adding to this complexity is the presence of uncertainty, which is often encoded into models via parameters or random variables. Any direct approach to analyze the impact of parametric variation on such expensive models is infeasible.
One approach to circumvent this limitation is to utilize hierarchies of models, each with differing computational costs and predictive fidelities. Research in the past few years has demonstrated that intelligent allocation of resources across this ensemble of models can produce predictions with much greater accuracy than concentrating all resources in a single model. Such multi-fidelity procedures hold the potential to optimally utilize ensembles of models to make predictions.

The main components of this proposed project address optimal resource allocation and robust and scalable model reduction, generation, and learning via low-rank multi-fidelity and multilevel procedures. The overall goal is the construction of surrogate models with accuracy guarantees that can be used in design optimization, inference, and general uncertainty quantification scenarios. The tasks associated with this project involve fundamental mathematical and algorithmic advances in low-rank multi-fidelity methods. Error certificates to ensure accuracy will be developed when possible. Kernel learning techniques will be employed to explore problem-dependent low-rank structure and optimize allocation of resources. Algorithmic methods to handle heterogeneous models, data, and parameter spaces will be developed resulting in a comprehensive framework for utilizing low-rank multi-fidelity methods.

The multi-fidelity procedures devised in this project will also aid in developing novel strategies for model comparison, ranking, discrimination, and genesis. Model comparison and ranking will enable development of a comprehensive multi-fidelity pipeline to automatically learn and update model hierarchies and fidelities. Model generation using the simulation data from a multi-fidelity pipeline allows the automated construction of model emulators that can more easily be explored to detect and exploit low-rank structure.

This project will explore usage of low-rank multi-fidelity methods in two main application areas. The first area is in robust design under uncertainty, which requires robust, accurate, and efficient forward model evaluations. The second area of application is in statistical inference, requiring computationally expensive exploration of posterior distributions. This project will demonstrate the utility of low-rank multi-fidelity methods in acceleration of robust design and inferential tasks. Problems addressed by the work in this project include simulations in topology optimization, nonlocal/fractional differential equation models, modeling of multi-physics solar power receivers, and supersonic channel flow.

UINTAH + HEDGEHOG -- Hybrid Task Graph Execution Library Development for Generalized Work Loads

Martin Berzins
The Overall Objective is to develop a new Uintah runtime environment that demonstrates a flexible approach for accommodating different task execution and state management strategies consistent with a starting point:

1. Uintah uses an asynchronous manytask (AMT) approach that has been shown to strong and weak scale to 256K cores with 16K GPUs on Titan and 768K cores on Mira, through its asynchronous adaptive and over-decomposition based runtime scheduler. This scheduler works on many different and diverse architectures, from many DOE and NSF leadership class machines to Chinas Sunway Tiahulight. In addition this AMT approach when combined with mesh coarsening allows for an efficient approach to resilience.

2. HTGS/Hedgehog is a high performance single node multi-CPU/GPU tasked based system developed at NIST. Internal state management and execution strategies at the level of a single node is maintained within an explicit task graph representation. HTGS/Hedgehog has produced good competitive results on a single node.

3. Demonstrating that the integration of two different task execution paradigms and the sharing of both local and global state can occur with minimal changes to either libraries.

The objective is to integrate the HTGS/Hedgehog Task Graph library into the Uintah Runtime. This new runtime would combine the global state management and multi-nodal execution characteristics of Uintah with the local single node execution facilities of HTGS/Hedgehog. This work would demonstrate and show how state management would be managed with these two different libraries. While the two libraries share many commonalities and architectural similarities, they are distinct in the underlying implementation. Understanding and developing a robust mechanism for sharing global and local state between the two libraries along with integrating the overall resource management strategies and task execution for multiple CPU/GPU architectures is the focus of this work.

The objectives will be carried out by first conducting feasibility studies between two different applications (3D structured grid application and an imaging analysis application) followed by the prototype implementation of new Uintah Scheduler that integrates the HTGS/Hedgehog library at the nodal level. The two different applications will be used demonstrate scalability and performance on both single node and multi-node systems. Finally, the proof-of-concept prototype Uintah Scheduler implementation will be transformed into a production level system in the third year of this effort.

Portable Applications Driven Approach to Scalability on Frontera and Future Exascale Systems

Martin Berzins
The present uncertainty in computer architectures requires software design to allow applications codes to both be able to scale across 20K to 100K nodes and to be able to run portably on a range of possible nodal architectures with a variety of processor technologies being involved, ranging from i86, ARM, GPU to possibly FPGAs. At the same time it isi important to use challenging applications to validate the software solutions and to ensure that they are realistic. This project led by Professor Martin Berzins will use the Frontera system to help address and demonstrate portability for an important class of engineering applications using the Uintah software.

Uintah software employs an asynchronous many task-based approach that has proved to be exceptionally robust at enabling complex engineering applications to run at scale on a broad range of architectures. As new and different architectures require not only the ability to execute tasks asynchronously but to deal with memory hierarchies and to execute efficiently on i86 architectures to GPUs and to a broad range of other possible architectures. Uintah use an approach based upon the Kokkos portability library that makes it possible to build a simple clean loop level interface that enables the loops themselves to execute efficiently on different architectures.

The work program will first port and evaluate existing Uintah architectures to Frontera and then consider new applications that apply the Uintah methodology to areas such as unstructured mesh calculations and particle methods applied to biomedical problems. The work program described here covers the application of these ideas to Frontera. The main effort will be through other funded projects, but any funding variability will be accomodated through an adaptive appropach to the applications space.

Collaborative Research: Detecting and Preventing Covid-19 with Privacy-Preserving Decentralized Machine Learning

Bao Wang
We are facing scientific challenges caused by the COVID-19, including detecting COVID-19 accurately and preventing its spread efficiently. Cutting-edge machine learning technologies, especially modern deep learning arts, provide feasible avenues to resolve these challenges. Deep learning-based computational imaging algorithms facilitate accurate and rapid COVID-19 diagnosis; sequential modeling with recurrent neural networks or transformers enables accurate and real-time COVID-19 spread prediction. However, most existing black-box deep learning research on the COVID-19 is the alchemy of turning unstructured data into gold and based on systematic trial and error. The current deep learning-based COVID-19 research raises many untrustworthy issues, including unreliable diagnosis, data privacy sacrifice, and lack of interpretability. Lacking interpretable and reliable predictions puts substantial strains on practitioners to leverage deep learning approaches to detect and prevent COVID-19. Data privacy constraints bring us many unraveling challenges. Thus, developing trustworthy machine learning algorithms while preserving data privacy is crucial to detect and prevent COVID-19.

We are a team of researchers with different expertise and common research interests, who jointly seek to develop theoretically principled decentralized machine learning algorithms that can provide reliable predictions. Furthermore, we focus on applying these machine learning algorithms to accurately and rapidly diagnose COVID-19 patients and predict the virus spread. We propose a challenging but walkable path towards developing privacy-preserving machine learning algorithms to detect and prevent COVID-19. We will integrate our expertise synergistically to develop privacy-preserving decentralized machine learning algorithms with performance guarantees and a high-throughput and low-latency software package to accurately and rapidly detect COVID-19 and effectively prevent its spread. As such, we propose three interconnected thrusts to develop novel neural network architectures based on mathematical principles, efficient privacy-preserving decentralized optimization algorithms, algorithms for spatiotemporal data forecasting and medical image processing and analysis, and an integrated software package to assist fighting against the coronavirus. Each thrust contains multiple theoretical explorations and numerical validation.

Intellectual Merit:
The proposal's intellectual merit include: (i) development of robust and mathematically principled recurrent neural networks for accurate real-time spatio-temporal forecasting, (ii) development of novel efficient federated and decentralized machine learning algorithms with a performance guarantee, (iii) leveraging the stochastic differential equations theory to develop new privacy-preserving machine learning mechanisms, (iv) construction of new epidemiology models-principled recurrent neural networks with accurate and interpretable predictions, (v) development of trustworthy deep learning-based frameworks for COVID-19 diagnosis from multi-modal medical measurements.

Broader Impacts:
The broader impacts of this project are in applying the proposed algorithms and their analysis over a wide range of science and engineering disciplines, such as scientific and medical image analysis, epidemic forecasting, patient monitoring, and microscopic imaging. The projects shall train a diverse body of the graduate and undergraduate students at Michigan State University, the University of Kentucky, and the University of Utah through collaborative education and research activities in applied mathematics, statistics, computer science, data science, physics, and social science. The project also plans to have research activities involving under-represented students in three universities located in three states. Besides the interdisciplinary collaboration across other institutions, we also aim to establish industrial partnerships to extend the proposed project's impact. The developed software will be shared with the general public through Github.

Sub-Pilot-Scale Production of High-Value Products for U.S. Coals

Chris Johnson
The primary objectives of this project are to: 1) provide sub-pilot scale verification of lab-scale developments on the production of isotropic and mesophase coal-tar pitch (CTP) for carbon fiber production, using coals from five U.S. coal-producing regions (UT, WY, WV, AK, IL); 2) investigate the production of a high-value b-SiC byproduct using residual coal char from the tar production process, and 3) develop an extensive database and suite of tools for data analysis and economic modeling, to relate process conditions to product quality, to assess the economic viability of coals from different regions for producing specific high-value products.

The University of Utah will use a 0.5 ton/day rotary reactor to pyrolyze coals to produce tars suitable for upgrading to coal tar pitch. The same reactor technology will be used in a second stage to perform the tar upgrading to either mesophase or isotropic pitch, depending on the properties of the original coal. The University of Wyoming will spin the product pitch into carbon fiber, to assess fiber quality arising from different coals and from different processing conditions. The solid char byproduct from coal pyrolysis will be used by the University of Wyoming to produce b-SiC. The University of Utah will work with Marshall University to develop a novel database, coupled with detailed economic models and analysis tools, to provide a means for understanding correlations between coal properties, process conditions and product quality, to allow assessment of the potential economic viability of coals from different regions for producing specific high-value products. Access to these some of these computational tools will become available to the public through a web-based community portal.

This effort is a major step towards providing a low-cost carbon fiber product from coal for potential use in automotive and other important markets, and will also lead to new economic development opportunities for communities with coal-based economies.

Experimental Characterization and Modeling of Failure in Post-Buckled Composite Stiffened Panels with a Scarf Repair

Alliance for Multiscale Modeling of Electronic Materials for an Energy Efficient Army

Mike Kirby
The objective of this Alliance is to conduct fundamental research to create MSME to support development of future electronic materials and devices for the Army. The U.S. Army Research Laboratory (ARL) envisions the MultiScale multidisciplinary Modeling of Electronic materials (MSME) Collaborative Research Alliance (CRA) which will bring together government, industrial, and academic institutions to undertake the fundamental research necessary to enable the quantitative understanding of electronic materials from the smallest to the largest relevant scales.

Augmented Design Through Analysis and Visualization Facilitating Better Designs and Enhanced Designers

In Situ Feature Extraction and Visualization from Discontinuous Galerkin Based High-Order Methods

Mike Kirby
The use of simulation science as a means of scientific inquiry is increasing at a tremendous rate. The process of mathematically modeling physical phenomena, estimating key modeling parameters, numerically approximating the solution, and computationally solving the resulting algorithm has inundated the scientific and engineering worlds, allowing for rapid advances in our understanding and utilization of the world around us. The efficacy of simulation science has been, in part, due to two critical components: (1) the identification and minimization of the error budget (e.g. modeling, discretization and uncertainty errors), and equally importantly, (2) evaluation mechanisms (such as visualization) by which the investigator assimilates the data produced through simulation. The latter allows for further refinement of the simulation science process (through model correction, increased numerical resolution, or algorithm debugging, etc.) and makes possible scientific statements about the physical phenomena being investigated.

Tremendous effort has been exerted over many decades in the pursuit of numerical methods that are both flexible and accurate, hence providing sufficient fidelity to be employed in the numerical solution of a large number of models, and sufficient analysis of accuracy to allow researchers to focus their attention on model refinement and uncertainty quantification. High-order finite element methods (also known as spectral/hp element methods), using either the continuous Galerkin or discontinuous Galerkin formulation, have reached a level of sophistication that allows them to be commonly applied to a diverse set of real-life engineering problems in computational solid mechanics, fluid dynamics, acoustics and electromagnetics. Many of the physical problems of interest are, unfortunately, not steady-state --- leading to simulations that must run for a long time (days, weeks and in some cases months). Thus, in the absence of creative solutions, datasets can easily consume all available storage and networking resources. Examples of such simulations within fluid dynamics include all simulations in which the fluid is in transition or fully turbulent. With regards to ARO interests, problems in turbo-machinery and rotorcraft, where aspects of the geometry are rotating and/or sliding past one other, fall into this category. High-order finite element methods are now beginning to be used to simulate these physical systems due to their inherent ability to capture complex structures (such as vortices) with little numerical dissipation and dispersion. The transient nature of these simulations complicates the data handling (post processing requires the time history) and renders single snap-shots of the solution insufficient to understand the time-varying nature of the physics.

Objective
Our research objectives are two-fold: (1) We will generate "high-order FEM" appropriate dimensionality reduction feature extraction methods such as vortex cores which can be accomplished as part of an in situ data processing pipeline. (2) Given the exploratory nature inherent in analyzing and visualizing transient phenomena, we may specify regions of interest in an in situ fashion within a simulation field based upon the visualization objective, extract and transmit the result of working on relevant high-order FEM information to our visualization system, and then reconstruct the visualization features of interest with the cognizance of V&V.

Publications in Scientific Computing:

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Finite element implementation of mechanochemical phenomena in neutral deformable porous media under finite deformation
G.A. Ateshian, M.B. Albro, S.A. Maas, J.A. Weiss. In Journal of Biomechanical Engineering, Vol. 133, No. 8, 2011.
DOI: 10.1115/1.4004810

Biological soft tissues and cells may be subjected to mechanical as well as chemical (osmotic) loading under their natural physiological environment or various experimental conditions. The interaction of mechanical and chemical effects may be very significant under some of these conditions, yet the highly nonlinear nature of the set of governing equations describing these mechanisms poses a challenge for the modeling of such phenomena. This study formulated and implemented a finite element algorithm for analyzing mechanochemical events in neutral deformable porous media under finite deformation. The algorithm employed the framework of mixture theory to model the porous permeable solid matrix and interstitial fluid, where the fluid consists of a mixture of solvent and solute. A special emphasis was placed on solute-solid matrix interactions, such as solute exclusion from a fraction of the matrix pore space (solubility) and frictional momentum exchange that produces solute hindrance and pumping under certain dynamic loading conditions. The finite element formulation implemented full coupling of mechanical and chemical effects, providing a framework where material properties and response functions may depend on solid matrix strain as well as solute concentration. The implementation was validated using selected canonical problems for which analytical or alternative numerical solutions exist. This finite element code includes a number of unique features that enhance the modeling of mechanochemical phenomena in biological tissues. The code is available in the public domain, open source finite element program FEBio (http://mrl.sci.utah.edu/software). [DOI: 10.1115/1.4004810]

From h to p Efficiently: Selecting the Optimal Spectral/hp Discretisation in Three Dimensions
C.D. Cantwell, S.J. Sherwin, R.M. Kirby, P.H.J. Kelly. In Mathematical Modelling of Natural Phenomena, Vol. 6, No. 3, pp. 84--96. 2011.

Finding consistent strain distributions in the glenohumeral capsule between two subjects: Implications for development of physical examinations
N.J. Drury, B.J. Ellis, J.A. Weiss, P.J. McMahon, R.E. Debski. In Journal of Biomechanics, Vol. 44, No. 4, pp. 607-613. February, 2011.
DOI: 10.1016/j.jbiomech.2010.11.018

The anterior-inferior glenohumeral capsule is the primary passive stabilizer to the glenohumeral joint during anterior dislocation. Physical examinations following dislocation are crucial for proper diagnosis of capsule pathology; however,they are not standardized for joint position which may lead to misdiagnoses and poor outcomes. To suggest joint positions for physical examinations where the stability provided by the capsule may be consistent among patients, the objective of this study was to evaluate the distribution of maximum principal strain on the anterior-inferior capsule using two validated subject-specific finite element models of the glenohumeral joint at clinically relevant joint positions. The joint positions with 25 N anterior load applied at 60° of glenohumeral abduction and 10°, 20°, 30° and 40° of external rotation resulted in distributions of strain that were similar between shoulders(r²≥0.7). Furthermore, those positions with 20-40° of external rotation resulted in capsule strains on the glenoid side of the anterior band of the inferior glenohumeral ligament that were significantly greater than in all other capsule regions. These findings suggest that anterior stability provided by the anterior-inferior capsule may be consistent among subjects at joint positions with 60° of glenohumeral abduction and a mid-range (20-40°) of external rotation, and that the glenoid side has the greatest contribution to stability at these joint positions. Therefore, it may be possible to establish standard joint positions for physical examinations that clinicians can use to effectively diagnose pathology in the anterior-inferior capsule following dislocation and lead to improved outcomes.

The capsule's contribution to total hip construct stability - a finite element analysis
J.M. Elkins, J.S. Stroud, M.J. Rudert, Y. Tochigi, D.R. Pedersen, B.J. Ellis, J.J. Callaghan, J.A. Weiss, T.D. Brown. In Journal of Orthopedic Research, Vol. 29, No. 11, Note: William Harris, MD Award, pp. 1642--1648. November, 2011.
DOI: 10.1002/jor.21435

Instability is a significant concern in total hip arthroplasty (THA), particularly when there is structural compromise of the capsule due to pre-existing pathology or due to necessities of surgical approach. An experimentally grounded fiber-direction-based finite element model of the hip capsule was developed, and was integrated with an established three-dimensional model of impingement/dislocation. Model validity was established by close similarity to results from a cadaveric experiment in a servohydraulic hip simulator. Parametric computational runs explored effects of graded levels of capsule thickness, of regional detachment from the capsule's femoral or acetabular insertions, of surgical incisions of capsule substance, and of capsule defect repairs. Depending strongly upon the specific site, localized capsule defects caused varying degrees of construct stability compromise, with several specific situations involving over 60\% decrement in dislocation resistance. Construct stability was returned substantially toward intact-capsule levels following well-conceived repairs, although the suture sites involved were often at substantial risk of failure. These parametric model results underscore the importance of retaining or robustly repairing capsular structures in THA, in order to maximize overall construct stability. © 2011 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 29:1642–1648, 2011

Defect Sampling in Global Error Estimation for ODEs and Method-Of-Lines PDEs Using Adjoint Methods
SCI Technical Report, L.T. Tran, M. Berzins. No. UUSCI-2011-006, SCI Institute, University of Utah, 2011.

A Conservered Developmental Patterning Network Produces Quantitatively Different Output in Multiple Species of Drosophila
C. Fowlkes, K. Eckenrode, M. Bragdon, M.D. Meyer, Z. Wunderlich, L. Simirenko, C. Luengo, S. Keranen, C. Henriquez, D. Knowles, M. Biggin, M. Eisen, A. DePace. In PLoS Genetics, Vol. 7, No. 10:e1002346, pp. 17 pages. October, 2011.

Differences in the level, timing, or location of gene expression can contribute to alternative phenotypes at the molecular and organismal level. Understanding the origins of expression differences is complicated by the fact that organismal morphology and gene regulatory networks could potentially vary even between closely related species. To assess the scope of such changes, we used high-resolution imaging methods to measure mRNA expression in blastoderm embryos of Drosophila yakuba and Drosophila pseudoobscura and assembled these data into cellular resolution atlases, where expression levels for 13 genes in the segmentation network are averaged into species-specific, cellular resolution morphological frameworks. We demonstrate that the blastoderm embryos of these species differ in their morphology in terms of size, shape, and number of nuclei. We present an approach to compare cellular gene expression patterns between species, while accounting for varying embryo morphology, and apply it to our data and an equivalent dataset for Drosophila melanogaster. Our analysis reveals that all individual genes differ quantitatively in their spatio-temporal expression patterns between these species, primarily in terms of their relative position and dynamics. Despite many small quantitative differences, cellular gene expression profiles for the whole set of genes examined are largely similar. This suggests that cell types at this stage of development are conserved, though they can differ in their relative position by up to 3-4 cell widths and in their relative proportion between species by as much as 5-fold. Quantitative differences in the dynamics and relative level of a subset of genes between corresponding cell types may reflect altered regulatory functions between species. Our results emphasize that transcriptional networks can diverge over short evolutionary timescales and that even small changes can lead to distinct output in terms of the placement and number of equivalent cells.

A fast iterative method for solving the Eikonal equation on triangulated surfaces
Z. Fu, W.-K. Jeong, Y. Pan, R.M. Kirby, R.T. Whitaker. In SIAM Journal of Scientific Computing, Vol. 33, No. 5, pp. 2468--2488. 2011.
DOI: 10.1137/100788951
PubMed Central ID: PMC3360588

This paper presents an efficient, fine-grained parallel algorithm for solving the Eikonal equation on triangular meshes. The Eikonal equation, and the broader class of Hamilton–Jacobi equations to which it belongs, have a wide range of applications from geometric optics and seismology to biological modeling and analysis of geometry and images. The ability to solve such equations accurately and efficiently provides new capabilities for exploring and visualizing parameter spaces and for solving inverse problems that rely on such equations in the forward model. Efficient solvers on state-of-the-art, parallel architectures require new algorithms that are not, in many cases, optimal, but are better suited to synchronous updates of the solution. In previous work [W. K. Jeong and R. T. Whitaker, SIAM J. Sci. Comput., 30 (2008), pp. 2512–2534], the authors proposed the fast iterative method (FIM) to efficiently solve the Eikonal equation on regular grids. In this paper we extend the fast iterative method to solve Eikonal equations efficiently on triangulated domains on the CPU and on parallel architectures, including graphics processors. We propose a new local update scheme that provides solutions of first-order accuracy for both architectures. We also propose a novel triangle-based update scheme and its corresponding data structure for efficient irregular data mapping to parallel single-instruction multiple-data (SIMD) processors. We provide detailed descriptions of the implementations on a single CPU, a multicore CPU with shared memory, and SIMD architectures with comparative results against state-of-the-art Eikonal solvers.

Formal Analysis of MPI-Based Parallel Programs: Present and Future
G. Gopalakrishnan, R.M. Kirby, S. Siegel, R. Thakur, W. Gropp, E. Lusk, B.R. de Supinski, M. Schultz, G. Bronevetsky. In Communications of the ACM, pp. (accepted). 2011.

Role of the acetabular labrum in load support across the hip joint
C.R. Henak, B.J. Ellis, M.D. Harris, A.E. Anderson, C.L. Peters, J.A. Weiss. In Journal of Biomechanics, Vol. 44, No. 12, pp. 2201-2206. 2011.

The relatively high incidence of labral tears among patients presenting with hip pain suggests that the acetabular labrum is often subjected to injurious loading in vivo. However, it is unclear whether the labrum participates in load transfer across the joint during activities of daily living. This study examined the role of the acetabular labrum in load transfer for hips with normal acetabular geometry and acetabular dysplasia using subject-specific finite element analysis. Models were generated from volumetric CT data and analyzed with and without the labrum during activities of daily living. The labrum in the dysplastic model supported 4–11\% of the total load transferred across the joint, while the labrum in the normal model supported only 1–2\% of the total load. Despite the increased load transferred to the acetabular cartilage in simulations without the labrum, there were minimal differences in cartilage contact stresses. This was because the load supported by the cartilage correlated with the cartilage contact area. A higher percentage of load was transferred to the labrum in the dysplastic model because the femoral head achieved equilibrium near the lateral edge of the acetabulum. The results of this study suggest that the labrum plays a larger role in load transfer and joint stability in hips with acetabular dysplasia than in hips with normal acetabular geometry.

Interpreting Performance Data Across Intuitive Domains
M. Schulz, J.A. Levine, P.-T. Bremer, T. Gamblin, V. Pascucci. In International Conference on Parallel Processing, Taipei, Taiwan, IEEE, pp. 206--215. 2011.
DOI: 10.1109/ICPP.2011.60

GPU-Based Interactive Cut-Surface Extraction From High-0rder Finite Element Fields
B. Nelson, R. Haimes, R.M. Kirby. In IEEE Transactions on Visualization and Computer Graphics (IEEE Visualization Issue), Vol. 17, No. 12, pp. 1803--1811. 2011.

We present a GPU-based ray-tracing system for the accurate and interactive visualization of cut-surfaces through 3D simulations of physical processes created from spectral/hp high-order finite element methods. When used by the numerical analyst to debug the solver, the ability for the imagery to precisely reflect the data is critical. In practice, the investigator interactively selects from a palette of visualization tools to construct a scene that can answer a query of the data. This is effective as long as the implicit contract of image quality between the individual and the visualization system is upheld. OpenGL rendering of scientific visualizations has worked remarkably well for exploratory visualization for most solver results. This is due to the consistency between the use of first-order representations in the simulation and the linear assumptions inherent in OpenGL (planar fragments and color-space interpolation). Unfortunately, the contract is broken when the solver discretization is of higher-order. There have been attempts to mitigate this through the use of spatial adaptation and/or texture mapping. These methods do a better job of approximating what the imagery should be but are not exact and tend to be view-dependent. This paper introduces new rendering mechanisms that specifically deal with the kinds of native data generated by high-order finite element solvers. The exploratory visualization tools are reassessed and cast in this system with the focus on image accuracy. This is accomplished in a GPU setting to ensure interactivity.

A Toolkit for Forward/Inverse Problems in Electrocardiography within the SCIRun Problem Solving Environment
B.M. Burton, J.D. Tate, B. Erem, D.J. Swenson, D.F. Wang, D.H. Brooks, P.M. van Dam, R.S. MacLeod. In Proceedings of the 2011 IEEE Int. Conf. Engineering and Biology Society (EMBC), pp. 267--270. 2011.
DOI: 10.1109/IEMBS.2011.6090052
PubMed ID: 22254301
PubMed Central ID: PMC3337752

Computational modeling in electrocardiography often requires the examination of cardiac forward and inverse problems in order to non-invasively analyze physiological events that are otherwise inaccessible or unethical to explore. The study of these models can be performed in the open-source SCIRun problem solving environment developed at the Center for Integrative Biomedical Computing (CIBC). A new toolkit within SCIRun provides researchers with essential frameworks for constructing and manipulating electrocardiographic forward and inverse models in a highly efficient and interactive way. The toolkit contains sample networks, tutorials and documentation which direct users through SCIRun-specific approaches in the assembly and execution of these specific problems.

Morse Set Classification and Hierarchical Refinement using Conley Index
Guoning Chen, Qingqing Deng, Andrzej Szymczak, Robert S. Laramee, and Eugene Zhang. In IEEE Transactions on Visualization and Computer Graphics (TVCG), Vol. 18, No. 5, pp. 767--782. June, 2011.
DOI: 10.1109/TVCG.2011.107
PubMed ID: 21690641

Morse decomposition provides a numerically stable topological representation of vector fields that is crucial for their rigorous interpretation. However, Morse decomposition is not unique, and its granularity directly impacts its computational cost. In this paper, we propose an automatic refinement scheme to construct the Morse Connection Graph (MCG) of a given vector field in a hierarchical fashion. Our framework allows a Morse set to be refined through a local update of the flow combinatorialization graph, as well as the connection regions between Morse sets. The computation is fast because the most expensive computation is concentrated on a small portion of the domain. Furthermore, the present work allows the generation of a topologically consistent hierarchy of MCGs, which cannot be obtained using a global method. The classification of the extracted Morse sets is a crucial step for the construction of the MCG, for which the Poincaré index is inadequate. We make use of an upper bound for the Conley index, provided by the Betti numbers of an index pair for a translation along the flow, to classify the Morse sets. This upper bound is sufficiently accurate for Morse set classification and provides supportive information for the automatic refinement process. An improved visualization technique for MCG is developed to incorporate the Conley indices. Finally, we apply the proposed techniques to a number of synthetic and real-world simulation data to demonstrate their utility.

A wildland fire modeling and visualization environment,
J. Mandel, J.D. Beezley, A. Kochanski, V.Y. Kondratenko, L. Zhang, E. Anderson, J. Daniels II, C.T. Silva, C.R. Johnson. In Proceedings of the Ninth Symposium on Fire and Forest Meteorology, pp. (published online). 2011.

Cardiac Position Sensitivity Study in the Electrocardiographic Forward Problem Using Stochastic Collocation and Boundary Element Methods
D.J. Swenson, S.E. Geneser, J.G. Stinstra, R.M. Kirby, R.S. MacLeod. In Annals of Biomedical Engineering, Vol. 39, No. 12, pp. 2900--2910. 2011.
DOI: 10.1007/s10439-011-0391-5
PubMed ID: 21909818
PubMed Central ID: PMC336204

The electrocardiogram (ECG) is ubiquitously employed as a diagnostic and monitoring tool for patients experiencing cardiac distress and/or disease. It is widely known that changes in heart position resulting from, for example, posture of the patient (sitting, standing, lying) and respiration significantly affect the body-surface potentials; however, few studies have quantitatively and systematically evaluated the effects of heart displacement on the ECG. The goal of this study was to evaluate the impact of positional changes of the heart on the ECG in the specific clinical setting of myocardial ischemia. To carry out the necessary comprehensive sensitivity analysis, we applied a relatively novel and highly efficient statistical approach, the generalized polynomial chaos-stochastic collocation method, to a boundary element formulation of the electrocardiographic forward problem, and we drove these simulations with measured epicardial potentials from whole-heart experiments. Results of the analysis identified regions on the body-surface where the potentials were especially sensitive to realistic heart motion. The standard deviation (STD) of ST-segment voltage changes caused by the apex of a normal heart, swinging forward and backward or side-to-side was approximately 0.2 mV. Variations were even larger, 0.3 mV, for a heart exhibiting elevated ischemic potentials. These variations could be large enough to mask or to mimic signs of ischemia in the ECG. Our results suggest possible modifications to ECG protocols that could reduce the diagnostic error related to postural changes in patients possibly suffering from myocardial ischemia.

Analysis of Large-Scale Scalar Data Using Hixels
D. Thompson, J.A. Levine, J.C. Bennett, P.-T. Bremer, A. Gyulassy, V. Pascucci, P.P. Pebay. In Proceedings of the 2011 IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV), Providence, RI, pp. 23--30. 2011.
DOI: 10.1109/LDAV.2011.6092313

Scalable Parallel Building Blocks for Custom Data Analysis
T. Peterka, R. Ross, A. Gyulassy, V. Pascucci, W. Kendall, H.-W. Shen, T.-Y. Lee, A. Chaudhuri. In Proceedings of the 2011 IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV), pp. 105--112. October, 2011.
DOI: 10.1109/LDAV.2011.6092324

We present a set of building blocks that provide scalable data movement capability to computational scientists and visualization researchers for writing their own parallel analysis. The set includes scalable tools for domain decomposition, process assignment, parallel I/O, global reduction, and local neighborhood communicationtasks that are common across many analysis applications. The global reduction is performed with a new algorithm, described in this paper, that efficiently merges blocks of analysis results into a smaller number of larger blocks. The merging is configurable in the number of blocks that are reduced in each round, the number of rounds, and the total number of resulting blocks. We highlight the use of our library in two analysis applications: parallel streamline generation and parallel Morse-Smale topological analysis. The first case uses an existing local neighborhood communication algorithm, whereas the latter uses the new merge algorithm.

Adaptive Extraction and Quantification of Geophysical Vortices
S. Williams, M. Petersen, P.-T. Bremer, M. Hecht, V. Pascucci, J. Ahrens, M. Hlawitschka, B. Hamann. In IEEE Transactions on Visualization and Computer Graphics, Proceedings of the 2011 IEEE Visualization Conference, Vol. 17, No. 12, pp. 2088--2095. 2011.

PIDX: Efficient Parallel I/O for Multi-resolution Multi-dimensional Scientific Datasets
S. Kumar, V. Vishwanath, P. Carns, B. Summa, G. Scorzelli, V. Pascucci, R. Ross, J. Chen, H. Kolla, R. Grout. In Proceedings of The IEEE International Conference on Cluster Computing, pp. 103--111. September, 2011.

Minimum Information about a Cardiac Electrophysiology Experiment (MICEE): Standardised reporting for model reproducibility, interoperability, and data sharing
T.A. Quinn, S. Granite, M.A. Allessie, C. Antzelevitch, C. Bollensdorff, G. Bub, R.A.B. Burton, E. Cerbai, P.S. Chen, M. Delmar, D. DiFrancesco, Y.E. Earm, I.R. Efimov, M. Egger, E. Entcheva, M. Fink, R. Fischmeister, M.R. Franz, A. Garny, W.R. Giles, T. Hannes, S.E. Harding, P.J. Hunter, s, G. Iribe, J. Jalife, C.R. Johnson, R.S. Kass, I. Kodama, G. Koren, P. Lord, V.S. Markhasin, S. Matsuoka, A.D. McCulloch, G.R. Mirams, G.E. Morley, S. Nattel, D. Noble, S.P. Olesen, A.V. Panfilov, N.A. Trayanova, U. Ravens, S. Richard, D.S. Rosenbaum, Y. Rudy, F. Sachs, F.B. Sachse, D.A. Saint, U. Schotten, O. Solovyova, P. Taggart, L. Tung, A. Varrò, P.G. Volders, K. Wang, J.N. Weiss, E. Wettwer, E. White, R. Wilders, R.L. Winslow, P. Kohl. In Progress in Biophysics and Molecular Biology, Vol. 107, No. 1, Elsevier, pp. 4--10. October, 2011.
DOI: 10.1016/j.pbiomolbio.2011.07.001
PubMed Central ID: PMC3190048

Cardiac experimental electrophysiology is in need of a well-defined Minimum Information Standard for recording, annotating, and reporting experimental data. As a step toward establishing this, we present a draft standard, called Minimum Information about a Cardiac Electrophysiology Experiment (MICEE). The ultimate goal is to develop a useful tool for cardiac electrophysiologists which facilitates and improves dissemination of the minimum information necessary for reproduction of cardiac electrophysiology research, allowing for easier comparison and utilisation of findings by others. It is hoped that this will enhance the integration of individual results into experimental, computational, and conceptual models. In its present form, this draft is intended for assessment and development by the research community. We invite the reader to join this effort, and, if deemed productive, implement the Minimum Information about a Cardiac Electrophysiology Experiment standard in their own work.

Keywords: Minimum Information Standard; Cardiac electrophysiology; Data sharing; Reproducibility; Integration; Computational modelling

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