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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Extending the SCIRun Problem Solving Environment to Large-Scale Applications
J. Knezevic, R.-P. Mundani, E. Rank, A. Khan, C.R. Johnson. In Proceedings of Applied Computing 2012, IADIS, pp. 171--178. October, 2012.

To make the most of current advanced computing technologies, experts in particular areas of science and engineering should be supported by sophisticated tools for carrying out computational experiments. The complexity of individual components of such tools should be hidden from them so they may concentrate on solving the specific problem within their field of expertise. One class of such tools are Problem Solving Environments (PSEs). The contribution of this paper refers to the idea of integration of an interactive computing framework applicable to different engineering applications into the SCIRun PSE in order to enable interactive real-time response of the computational model to user interaction even for large-scale problems. While the SCIRun PSE allows for real-time computational steering, we propose extending this functionality to a wider range of applications and larger scale problems. With only minor code modifications the proposed system allows each module scheduled for execution in a dataflow-based simulation to be automatically interrupted and re-scheduled. This rescheduling allows one to keep the relation between the user interaction and its immediate effect transparent independent of the problem size, thus, allowing for the intuitive and interactive exploration of simulation results.

Keywords: scirun

Smoothness-Increasing Accuracy-Conserving (SIAC) Filtering for discontinuous Galerkin Solutions: Improved Errors Versus Higher-Order Accuracy
J. King, H. Mirzaee, J.K. Ryan, R.M. Kirby. In Journal of Scientific Computing, Vol. 53, pp. 129--149. 2012.
DOI: 10.1007/s10915-012-9593-8

Smoothness-increasing accuracy-conserving (SIAC) filtering has demonstrated its effectiveness in raising the convergence rate of discontinuous Galerkin solutions from order k + 1/2 to order 2k + 1 for specific types of translation invariant meshes (Cockburn et al. in Math. Comput. 72:577–606, 2003; Curtis et al. in SIAM J. Sci. Comput. 30(1):272– 289, 2007; Mirzaee et al. in SIAM J. Numer. Anal. 49:1899–1920, 2011). Additionally, it improves the weak continuity in the discontinuous Galerkin method to k - 1 continuity. Typically this improvement has a positive impact on the error quantity in the sense that it also reduces the absolute errors. However, not enough emphasis has been placed on the difference between superconvergent accuracy and improved errors. This distinction is particularly important when it comes to understanding the interplay introduced through meshing, between geometry and filtering. The underlying mesh over which the DG solution is built is important because the tool used in SIAC filtering—convolution—is scaled by the geometric mesh size. This heavily contributes to the effectiveness of the post-processor. In this paper, we present a study of this mesh scaling and how it factors into the theoretical errors. To accomplish the large volume of post-processing necessary for this study, commodity streaming multiprocessors were used; we demonstrate for structured meshes up to a 50× speed up in the computational time over traditional CPU implementations of the SIAC filter.

Multiscale Modeling of High Explosives for Transportation Accidents
J.R. Peterson, J.C. Beckvermit, T. Harman, M. Berzins, C.A. Wight. In Proceedings of the 1st Conference of the Extreme Science and Engineering Discovery Environment: Bridging from the eXtreme to the campus and beyond, 2012.
DOI: 10.1145/2335755.2335828

The development of a reaction model to simulate the accidental detonation of a large array of seismic boosters in a semi-truck subject to fire is considered. To test this model large scale simulations of explosions and detonations were performed by leveraging the massively parallel capabilities of the Uintah Computational Framework and the XSEDE computational resources. Computed stress profiles in bulk-scale explosive materials were validated using compaction simulations of hundred micron scale particles and found to compare favorably with experimental data. A validation study of reaction models for deflagration and detonation showed that computational grid cell sizes up to 10 mm could be used without loss of fidelity. The Uintah Computational Framework shows linear scaling up to 180K cores which combined with coarse resolution and validated models will now enable simulations of semi-truck scale transportation accidents for the first time.

Radiation Modeling Using the Uintah Heterogeneous CPU/GPU Runtime System
A. Humphrey, Q. Meng, M. Berzins, T. Harman. In Proceedings of the first conference of the Extreme Science and Engineering Discovery Environment (XSEDE'12), Association for Computing Machinery, 2012.
DOI: 10.1145/2335755.2335791

The Uintah Computational Framework was developed to provide an environment for solving fluid-structure interaction problems on structured adaptive grids on large-scale, long-running, data-intensive problems. Uintah uses a combination of fluid-flow solvers and particle-based methods for solids, together with a novel asynchronous task-based approach with fully automated load balancing. Uintah demonstrates excellent weak and strong scalability at full machine capacity on XSEDE resources such as Ranger and Kraken, and through the use of a hybrid memory approach based on a combination of MPI and Pthreads, Uintah now runs on up to 262k cores on the DOE Jaguar system. In order to extend Uintah to heterogeneous systems, with ever-increasing CPU core counts and additional onnode GPUs, a new dynamic CPU-GPU task scheduler is designed and evaluated in this study. This new scheduler enables Uintah to fully exploit these architectures with support for asynchronous, outof- order scheduling of both CPU and GPU computational tasks. A new runtime system has also been implemented with an added multi-stage queuing architecture for efficient scheduling of CPU and GPU tasks. This new runtime system automatically handles the details of asynchronous memory copies to and from the GPU and introduces a novel method of pre-fetching and preparing GPU memory prior to GPU task execution. In this study this new design is examined in the context of a developing, hierarchical GPUbased ray tracing radiation transport model that provides Uintah with additional capabilities for heat transfer and electromagnetic wave propagation. The capabilities of this new scheduler design are tested by running at large scale on the modern heterogeneous systems, Keeneland and TitanDev, with up to 360 and 960 GPUs respectively. On these systems, we demonstrate significant speedups per GPU against a standard CPU core for our radiation problem.

Keywords: Uintah, hybrid parallelism, scalability, parallel, adaptive, GPU, heterogeneous systems, Keeneland, TitanDev

Radiation Modeling Using the Uintah Heterogeneous CPU/GPU Runtime System
SCI Technical Report, A. Humphrey, Q. Meng, M. Berzins, T. Harman. No. UUSCI-2012-003, SCI Institute, University of Utah, 2012.

Keywords: csafe, uintah

Scalable Large-scale Fluid-structure Interaction Solvers in the Uintah Framework via Hybrid Task-based Parallelism Algorithms
SCI Technical Report, Q. Meng, M. Berzins. No. UUSCI-2012-004, SCI Institute, University of Utah, 2012.

Uintah is a software framework that provides an environment for solving fluid-structure interaction problems on structured adaptive grids on large-scale science and engineering problems involving the solution of partial differential equations. Uintah uses a combination of fluid-flow solvers and particle-based methods for solids, together with adaptive meshing and a novel asynchronous task-based approach with fully automated load balancing. When applying Uintah to fluid-structure interaction problems with mesh refinement, the combination of adaptive meshing and the movement of structures through space present a formidable challenge in terms of achieving scalability on large-scale parallel computers. With core counts per socket continuing to grow along with the prospect of less memory per core, adopting a model that uses MPI to communicate between nodes and a shared memory model on-node is one approach to achieve scalability at full machine capacity on current and emerging large-scale systems. For this approach to be successful, it is necessary to design data-structures that large numbers of cores can simultaneously access without contention. These data structures and algorithms must also be designed to avoid the overhead involved with locks and other synchronization primitives when running on large number of cores per node, as contention for acquiring locks quickly becomes untenable. This scalability challenge is addressed here for Uintah, by the development of new hybrid runtime and scheduling algorithms combined with novel lockfree data structures, making it possible for Uintah to achieve excellent scalability for a challenging fluid-structure problem with mesh refinement on as many as 260K cores.

Keywords: uintah, csafe

Large Scale Parallel Solution of Incompressible Flow Problems using Uintah and hypre
SCI Technical Report, J. Schmidt, M. Berzins, J. Thornock, T. Saad, J. Sutherland. No. UUSCI-2012-002, SCI Institute, University of Utah, 2012.

The Uintah Software framework was developed to provide an environment for solving fluid-structure interaction problems on structured adaptive grids on large-scale, long-running, data-intensive problems. Uintah uses a combination of fluid-flow solvers and particle-based methods for solids together with a novel asynchronous task-based approach with fully automated load balancing. As Uintah is often used to solve compressible, low-Mach combustion applications, it is important to have a scalable linear solver. While there are many such solvers available, the scalability of those codes varies greatly. The hypre software offers a range of solvers and pre-conditioners for different types of grids. The weak scalability of Uintah and hypre is addressed for particular examples when applied to an incompressible flow problem relevant to combustion applications. After careful software engineering to reduce start-up costs, much better than expected weak scalability is seen for up to 100K cores on NSFs Kraken architecture and up to 200K+ cores, on DOEs new Titan machine.

Keywords: uintah, csafe

Biomedical Visual Computing: Case Studies and Challenges
C.R. Johnson. In IEEE Computing in Science and Engineering, Vol. 14, No. 1, pp. 12--21. 2012.
PubMed ID: 22545005
PubMed Central ID: PMC3336198

Computer simulation and visualization are having a substantial impact on biomedicine and other areas of science and engineering. Advanced simulation and data acquisition techniques allow biomedical researchers to investigate increasingly sophisticated biological function and structure. A continuing trend in all computational science and engineering applications is the increasing size of resulting datasets. This trend is also evident in data acquisition, especially in image acquisition in biology and medical image databases.

For example, in a collaboration between neuroscientist Robert Marc and our research team at the University of Utah's Scientific Computing and Imaging (SCI) Institute (www.sci.utah.edu), we're creating datasets of brain electron microscopy (EM) mosaics that are 16 terabytes in size. However, while there's no foreseeable end to the increase in our ability to produce simulation data or record observational data, our ability to use this data in meaningful ways is inhibited by current data analysis capabilities, which already lag far behind. Indeed, as the NIH-NSF Visualization Research Challenges report notes, to effectively understand and make use of the vast amounts of data researchers are producing is one of the greatest scientific challenges of the 21st century.

Visual data analysis involves creating images that convey salient information about underlying data and processes, enabling the detection and validation of expected results while leading to unexpected discoveries in science. This allows for the validation of new theoretical models, provides comparison between models and datasets, enables quantitative and qualitative querying, improves interpretation of data, and facilitates decision making. Scientists can use visual data analysis systems to explore \"what if\" scenarios, define hypotheses, and examine data under multiple perspectives and assumptions. In addition, they can identify connections between numerous attributes and quantitatively assess the reliability of hypotheses. In essence, visual data analysis is an integral part of scientific problem solving and discovery.

As applied to biomedical systems, visualization plays a crucial role in our ability to comprehend large and complex data-data that, in two, three, or more dimensions, convey insight into many diverse biomedical applications, including understanding neural connectivity within the brain, interpreting bioelectric currents within the heart, characterizing white-matter tracts by diffusion tensor imaging, and understanding morphology differences among different genetic mice phenotypes.

Keywords: kaust

Status of Release of the Uintah Computational Framework
SCI Technical Report, M. Berzins. No. UUSCI-2012-001, SCI Institute, University of Utah, 2012.

This report provides a summary of the status of the Uintah Computation Framework (UCF) software. Uintah is uniquely equipped to tackle large-scale multi-physics science and engineering problems on disparate length and time scales. The Uintah framework makes it possible to run adaptive computations on modern HPC architectures with tens and now hundreds of thousands of cores with complex communication/memory hierarchies. Uintah was orignally developed in the University of Utah Center for Simulation of Accidental Fires and Explosions (C-SAFE), a DOE-funded academic alliance project and then extended to the broader NSF snd DOE science and engineering communities. As Uintah is applicable to a wide range of engineering problems that involve fl uid-structure interactions with highly deformable structures it is used for a number of NSF-funded and DOE engineering projects. In this report the Uintah framework software is outlined and typical applications are illustrated. Uintah is open-source software that is available through the MIT open-source license at http://www.uintah.utah.edu/.

Fast, Effective BVH Updates for Animated Scenes
D. Kopta, T. Ize, J. Spjut, E. Brunvand, A. Davis, A. Kensler. In Proceedings of the Symposium on Interactive 3D Graphics and Games (I3D '12), pp. 197--204. 2012.
DOI: 10.1145/2159616.2159649

Bounding volume hierarchies (BVHs) are a popular acceleration structure choice for animated scenes rendered with ray tracing. This is due to the relative simplicity of refitting bounding volumes around moving geometry. However, the quality of such a refitted tree can degrade rapidly if objects in the scene deform or rearrange significantly as the animation progresses, resulting in dramatic increases in rendering times and a commensurate reduction in the frame rate. The BVH could be rebuilt on every frame, but this could take significant time. We present a method to efficiently extend refitting for animated scenes with tree rotations, a technique previously proposed for off-line improvement of BVH quality for static scenes. Tree rotations are local restructuring operations which can mitigate the effects that moving primitives have on BVH quality by rearranging nodes in the tree during each refit rather than triggering a full rebuild. The result is a fast, lightweight, incremental update algorithm that requires negligible memory, has minor update times, parallelizes easily, avoids significant degradation in tree quality or the need for rebuilding, and maintains fast rendering times. We show that our method approaches or exceeds the frame rates of other techniques and is consistently among the best options regardless of the animated scene.

Understanding Quasi-Periodic Fieldlines and Their Topology in Toroidal Magnetic Fields
A.R. Sanderson, G. Chen, X. Tricoche, E. Cohen. In Topological Methods in Data Analysis and Visualization II, Edited by R. Peikert and H. Carr and H. Hauser and R. Fuchs, Springer, pp. 125--140. 2012.
DOI: 10.1007/478-3-642-23175-9

Adaptive High-Order Discontinuous Galerkin Solution of Elastohydrodynamic Lubrication Point Contact Problems
H. Lu, M. Berzins, C.E. Goodyer, P.K. Jimack. In Advances in Engineering Software, Vol. 45, No. 1, pp. 313--324. 2012.
DOI: 10.1016/j.advengsoft.2011.10.006

This paper describes an adaptive implementation of a high order Discontinuous Galerkin (DG) method for the solution of elastohydrodynamic lubrication (EHL) point contact problems. These problems arise when modelling the thin lubricating film between contacts which are under sufficiently high pressure that the elastic deformation of the contacting elements cannot be neglected. The governing equations are highly nonlinear and include a second order partial differential equation that is derived via the thin-film approximation. Furthermore, the problem features a free boundary, which models where cavitation occurs, and this is automatically captured as part of the solution process. The need for spatial adaptivity stems from the highly variable length scales that are present in typical solutions. Results are presented which demonstrate both the effectiveness and the limitations of the proposed adaptive algorithm.

Keywords: Elastohydrodynamic lubrication, Discontinuous Galerkin, High polynomial degree, h-adaptivity, Nonlinear systems

DAG-Based Software Frameworks for PDEs
M. Berzins, Q. Meng, J. Schmidt, J.C. Sutherland. In Proceedings of Euro-Par 2011 Workshops, Part I, Lecture Notes in Computer Science (LNCS) 7155, Springer-Verlag Berlin Heidelberg, pp. 324--333. August, 2012.

The task-based approach to software and parallelism is well-known and has been proposed as a potential candidate, named the silver model, for exascale software. This approach is not yet widely used in the large-scale multi-core parallel computing of complex systems of partial differential equations. After surveying task-based approaches we investigate how well the Uintah software and an extension named Wasatch fit in the task-based paradigm and how well they perform on large scale parallel computers. The conclusion is that these approaches show great promise for petascale but that considerable algorithmic challenges remain.

Keywords: DOD, Uintah, CSAFE

An optimization framework for inversely estimating myocardial transmembrane potentials and localizing ischemia
D. Wang, R.M. Kirby, R.S. Macleod, C.R. Johnson. In Proceedings of the International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS), pp. 1680--1683. 2011.
DOI: 10.1109/IEMBS.2011.6090483
PubMed ID: 22254648
PubMed Central ID: PMC3336368

By combining a static bidomain heart model with a torso conduction model, we studied the inverse electrocardiographic problem of computing the transmembrane potentials (TMPs) throughout the myocardium from a body-surface potential map, and then used the recovered potentials to localize myocardial ischemia. Our main contribution is solving the inverse problem within a constrained optimization framework, which is a generalization of previous methods for calculating transmembrane potentials. The framework offers ample flexibility for users to apply various physiologically-based constraints, and is well supported by mature algorithms and solvers developed by the optimization community. By avoiding the traditional inverse ECG approach of building the lead-field matrix, the framework greatly reduces computation cost and, by setting the associated forward problem as a constraint, the framework enables one to flexibly set individualized resolutions for each physical variable, a desirable feature for balancing model accuracy, ill-conditioning and computation tractability. Although the task of computing myocardial TMPs at an arbitrary time instance remains an open problem, we showed that it is possible to obtain TMPs with moderate accuracy during the ST segment by assuming all cardiac cells are at the plateau phase. Moreover, the calculated TMPs yielded a good estimate of ischemic regions, which was of more clinical interest than the voltage values themselves. We conducted finite element simulations of a phantom experiment over a 2D torso model with synthetic ischemic data. Preliminary results indicated that our approach is feasible and suitably accurate for the common case of transmural myocardial ischemia.

Cyber Science and Engineering: A Report of the National Science Foundation Advisory Committee for Cyberinfrastructure Task Force on Grand Challenges
J.T. Oden, O. Ghattas, J.L. King, B.I. Schneider, K. Bartschat, F. Darema, J. Drake, T. Dunning, D. Estep, S. Glotzer, M. Gurnis, C.R. Johnson, D.S. Katz, D. Keyes, S. Kiesler, S. Kim, J. Kinter, G. Klimeck, C.W. McCurdy, R. Moser, C. Ott, A. Patra, L. Petzold, T. Schlick, K. Schulten, V. Stodden, J. Tromp, M. Wheeler, S.J. Winter, C. Wu, K. Yelick. Note: NSF Report, 2011.

This document contains the findings and recommendations of the NSF – Advisory Committee for Cyberinfrastructure Task Force on Grand Challenges addressed by advances in Cyber Science and Engineering. The term Cyber Science and Engineering (CS&E) is introduced to describe the intellectual discipline that brings together core areas of science and engineering, computer science, and computational and applied mathematics in a concerted effort to use the cyberinfrastructure (CI) for scientific discovery and engineering innovations; CS&E is computational and data-based science and engineering enabled by CI. The report examines a host of broad issues faced in addressing the Grand Challenges of science and technology and explores how those can be met by advances in CI. Included in the report are recommendations for new programs and initiatives that will expand the portfolio of the Office of Cyberinfrastructure and that will be critical to advances in all areas of science and engineering that rely on the CI.

Advisory Committee for CyberInfrastructure Task Force on Software for Science and Engineering
D. Keyes, V. Taylor, T. Hey, S. Feldman, G. Allen, P. Colella, P. Cummings, F. Darema, J. Dongarra, T. Dunning, M. Ellisman, I. Foster, W. Gropp, C.R. Johnson, C. Kamath, R. Madduri, M. Mascagni, S.G. Parker, P. Raghavan, A. Trefethen, S. Valcourt, A. Patra, F. Choudhury, C. Cooper, P. McCartney, M. Parashar, T. Russell, B. Schneider, J. Schopf, N. Sharp. Note: NSF Report, 2011.

The Software for Science and Engineering (SSE) Task Force commenced in June 2009 with a charge that consisted of the following three elements:

Identify specific needs and opportunities across the spectrum of scientific software infrastructure. Characterize the specific needs and analyze technical gaps and opportunities for NSF to meet those needs through individual and systemic approaches. Design responsive approaches. Develop initiatives and programs led (or co-led) by NSF to grow, develop, and sustain the software infrastructure needed to support NSF’s mission of transformative research and innovation leading to scientific leadership and technological competitiveness. Address issues of institutional barriers. Anticipate, analyze and address both institutional and exogenous barriers to NSF’s promotion of such an infrastructure.

The SSE Task Force members participated in bi-weekly telecons to address the given charge. The telecons often included additional distinguished members of the scientific community beyond the task force membership engaged in software issues, as well as personnel from federal agencies outside of NSF who manage software programs. It was quickly acknowledged that a number of reports loosely and tightly related to SSE existed and should be leveraged. By September 2009, the task formed had formed three subcommittees focused on the following topics: (1) compute-intensive science, (2) data-intensive science, and (3) software evolution.

Sensitivity Analysis for the Optimization of Radiofrequency Ablation in the Presence of Material Parameter Uncertainty
I. Altrogge, T. Preusser, T. Kroeger, S. Haase, T. Paetz, R.M. Kirby. In International Journal for Uncertainty Quantification, 2011.

We present a sensitivity analysis of the optimization of the probe placement in radiofrequency (RF) ablation which takes the uncertainty associated with bio-physical tissue properties (electrical and thermal conductivity) into account. Our forward simulation of RF ablation is based upon a system of partial differential equations (PDEs) that describe the electric potential of the probe and the steady state of the induced heat. The probe placement is optimized by minimizing a temperature-based objective function such that the volume of destroyed tumor tissue is maximized. The resulting optimality system is solved with a multi-level gradient descent approach. By evaluating the corresponding optimality system for certain realizations of tissue parameters (i.e. at certain, well-chosen points in the stochastic space) the sensitivity of the system can be analyzed with respect to variations in the tissue parameters. For the interpolation in the stochastic space we use a stochastic finite element approach with piecewise multilinear ansatz functions on adaptively refined, hierarchical grids. We underscore the significance of the approach by applying the optimization to CT data obtained from a real RF ablation case.

Keywords: netl, stochastic sensitivity analysis, stochastic partial dierential equations, stochastic nite element method, adaptive sparse grid, heat transfer, multiscale modeling, representation of uncertainty

Implementation and Verification of a Nodally-Integrated Tetrahedral Element in FEBio
SCI Technical Report, S.A. Maas, B.J. Ellis, D.S. Rawlins, L.T. Edgar, C.R. Henak, J.A. Weiss. No. UUSCI-2011-007, SCI Institute, University of Utah, 2011.

Finite element simulations in computational biomechanics commonly require the discretization of extremely complicated geometries. Creating meshes for these complex geometries can be very difficult and time consuming using hexahedral elements. Automatic meshing algorithms exist for tetrahedral elements, but these elements often have numerical problems that discourage their use in complex finite element models. To overcome these problems we have implemented a stabilized, nodally-integrated tetrahedral element formulation in FEBio, our in-house developed finite element code, allowing researchers to use linear tetrahedral elements in their models and still obtain accurate solutions. In addition to facilitating automatic mesh generation, this also allows researchers to use mesh refinement algorithms which are fairly well developed for tetrahedral elements but not so much for hexahedral elements. In this document, the implementation of the stabilized, nodallyintegrated, tetrahedral element, named the \"UT4 element\", is described. Two slightly different variations of the nodally integrated tetrahedral element are considered. In one variation the entire virtual work is stabilized and in the other one the stabilization is only applied to the isochoric part of the virtual work. The implementation of both formulations has been verified and the convergence behavior illustrated using the patch test and three verification problems. Also, a model from our laboratory with very complex geometry is discretized and analyzed using the UT4 element to show its utility for a problem from the biomechanics literature. The convergence behavior of the UT4 element does vary depending on problem, tetrahedral mesh structure and choice of formulation parameters, but the results from the verification problems should assure analysts that a converged solution using the UT4 element can be obtained that is more accurate than the solution from a classical linear tetrahedral formulation.

Keywords: MRL

Dark Regions of No-Reflow on Late Gadolinium Enhancement Magnetic Resonance Imaging Result in Scar Formation After Atrial Fibrillation Ablation
C.J. McGann, E.G. Kholmovski, J.J. Blauer, S. Vijayakumar, T.S. Haslam, J.E. Cates, E.V. DiBella, N.S. Burgon, B. Wilson, A.J. Alexander, M.W. Prastawa, M. Daccarett, G. Vergara, N.W. Akoum, D.L. Parker, R.S. MacLeod, N.F. Marrouche. In Journal of the American College of Cardiology, Vol. 58, No. 2, pp. 177--185. 2011.
DOI: 10.1016/j.jacc.2011.04.008
PubMed ID: 21718914

Objectives: The aim of this study was to assess acute ablation injuries seen on late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) immediately post-ablation (IPA) and the association with permanent scar 3 months post-ablation (3moPA).

Background: Success rates for atrial fibrillation catheter ablation vary significantly, in part because of limited information about the location, extent, and permanence of ablation injury at the time of procedure. Although the amount of scar on LGE MRI months after ablation correlates with procedure outcomes, early imaging predictors of scar remain elusive.

Methods: Thirty-seven patients presenting for atrial fibrillation ablation underwent high-resolution MRI with a 3-dimensional LGE sequence before ablation, IPA, and 3moPA using a 3-T scanner. The acute left atrial wall injuries on IPA scans were categorized as hyperenhancing (HE) or nonenhancing (NE) and compared with scar 3moPA.

Results: Heterogeneous injuries with HE and NE regions were identified in all patients. Dark NE regions in the left atrial wall on LGE MRI demonstrate findings similar to the \"no-reflow\" phenomenon. Although the left atrial wall showed similar amounts of HE, NE, and normal tissue IPA (37.7 ± 13\%, 34.3 ± 14\%, and 28.0 ± 11\%, respectively; p = NS), registration of IPA injuries with 3moPA scarring demonstrated that 59.0 ± 19\% of scar resulted from NE tissue, 30.6 ± 15\% from HE tissue, and 10.4 ± 5\% from tissue identified as normal. Paired t-test comparisons were all statistically significant among NE, HE, and normal tissue types (p less than 0.001). Arrhythmia recurrence at 1-year follow-up correlated with the degree of wall enhancement 3moPA (p = 0.02).

Conclusion: Radiofrequency ablation results in heterogeneous injury on LGE MRI with both HE and NE wall lesions. The NE lesions demonstrate no-reflow characteristics and reveal a better predictor of final scar at 3 months. Scar correlates with procedure outcomes, further highlighting the importance of early scar prediction. (J Am Coll Cardiol 2011;58:177–85) © 2011 by the American College of Cardiology Foundation

Smoothness-Increasing Accuracy-Conserving (SIAC) Postprocessing for Discontinuous Galerkin Solutions Over Structured Triangular Meshes
H. Mirzaee, Liangyue, J.K. Ryan, R.M. Kirby. In SIAM Journal of Numerical Analysis, Vol. 49, No. 5, pp. 1899--1920. 2011.

Theoretically and computationally, it is possible to demonstrate that the order of accuracy of a discontinuous Galerkin (DG) solution for linear hyperbolic equations can be improved from order k+1 to 2k+1 through the use of smoothness-increasing accuracy-conserving (SIAC) filtering. However, it is a computationally complex task to perform this in an efficient manner, which becomes an even greater issue considering nonquadrilateral mesh structures. In this paper, we present an extension of this SIAC filter to structured triangular meshes. The basic theoretical assumption in the previous implementations of the postprocessor limits the use to numerical solutions solved over a quadrilateral mesh. However, this assumption is restrictive, which in turn complicates the application of this postprocessing technique to general tessellations. Additionally, moving from quadrilateral meshes to triangulated ones introduces more complexity in the calculations as the number of integrations required increases. In this paper, we extend the current theoretical results to variable coefficient hyperbolic equations over structured triangular meshes and demonstrate the effectiveness of the application of this postprocessor to structured triangular meshes as well as exploring the effect of using inexact quadrature. We show that there is a direct theoretical extension to structured triangular meshes for hyperbolic equations with bounded variable coefficients. This is a challenging first step toward implementing SIAC filters for unstructured tessellations. We show that by using the usual B-spline implementation, we are able to improve on the order of accuracy as well as decrease the magnitude of the errors. These results are valid regardless of whether exact or inexact integration is used. The results here demonstrate that it is still possible, both theoretically and computationally, to improve to 2k+1 over the DG solution itself for structured triangular meshes.

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