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

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:

Page 6 of 22

Start
Prev
1
2
3
4
5
6
7
8
9
10
Next
End

An Orthogonality Property of the Legendre Polynomials
L. Bos, A. Narayan, N. Levenberg, F. Piazzon. In Constructive Approximation, Vol. 45, No. 1, pp. 65--81. Feb, 2017.
ISSN: 0176-4276, 1432-0940
DOI: 10.1007/s00365-015-9321-3

We give a remarkable additional othogonality property of the classical Legendre polynomials on the real interval [−1,1]: polynomials up to degree n from this family are mutually orthogonal under the arcsine measure weighted by the degree-n normalized Christoffel function

Nonlinear Stability of the MPM Method
M. Berzins. In V International Conference on Particle-based Methods – Fundamentals and Applications. PARTICLES 2017, Edited by P. Wriggers, M. Bischoff, E. O˜nate, D.R.J. Owen, & T. Zohdi, pp. 671--682. 2017.

The Material Point Method (MPM) has been very successful in providing solutions to many challenging problems involving large deformations. The nonlinear nature of MPM makes it necessary to use a full nonlinear stability analysis to determine a stable timestep. The stability analysis of Spigler and Vianello is adapted to MPM and used to derive a stable timestep bound for a model problem. This bound is contrasted against a traditional CFL bound.

Optimization Strategies for WRF Single-Moment 6-Class Microphysics Scheme (WSM6) on Intel Microarchitectures
T.A.J. Ouermi, A. Knoll, R.M. Kirby, M. Berzins. In Proceedings of the fifth international symposium on computing and networking (CANDAR 17). Awarded Best Paper , IEEE, 2017.

Optimizations in the petascale era require modifications of existing codes to take advantage of new architectures with large core counts and SIMD vector units. This paper examines high-level and low-level optimization strategies for numerical weather prediction (NWP) codes. These strategies employ thread-local structures of arrays (SOA) and an OpenMP directive such as OMP SIMD. These optimization approaches are applied to the Weather Research Forecasting single-moment 6-class microphysics schemes (WSM6) in the US Navy NEPTUNE system. The results of this study indicate that the high-level approach with SOA and low-level OMP SIMD improves thread and vector parallelism by increasing data and temporal locality. The modified version of WSM6 runs 70x faster than the original serial code. This improvement is about 23.3x faster than the performance achieved by Ouermi et al., and 14.9x faster than the performance achieved by Michalakes et al.

Addressing Global Data Dependencies in Heterogeneous Asynchronous Runtime Systems on GPUs. Awarded Best Paper
B. Peterson, A. Humphrey, J. Schmidt, M. Berzins. In Proceedings of the Third International Workshop on Extreme Scale Programming Models and Middleware - ESPM2'17, ACM, 2017.
DOI: 10.1145/3152041.3152082

Large-scale parallel applications with complex global data dependencies beyond those of reductions pose significant scalability challenges in an asynchronous runtime system. Internodal challenges include identifying the all-to-all communication of data dependencies among the nodes. Intranodal challenges include gathering together these data dependencies into usable data objects while avoiding data duplication. This paper addresses these challenges within the context of a large-scale, industrial coal boiler simulation using the Uintah asynchronous many-task runtime system on GPU architectures. We show significant reduction in time spent analyzing data dependencies through refinements in our dependency search algorithm. Multiple task graphs are used to eliminate subsequent analysis when task graphs change in predictable and repeatable ways. Using a combined data store and task scheduler redesign reduces data dependency duplication ensuring that problems fit within host and GPU memory. These modifications did not require any changes to application code or sweeping changes to the Uintah runtime system. We report results running on the DOE Titan system on 119K CPU cores and 7.5K GPUs simultaneously. Our solutions can be generalized to other task dependency problems with global dependencies among thousands of nodes which must be processed efficiently at large scale.

Massively Parallel Simulations of Spread of Infectious Diseases over Realistic Social Networks
A. Bhatele, J. Yeom, N. Jain, C. J. Kuhlman, Y. Livnat, K. R. Bisset, L. V. Kale, M. V. Marathe. In 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), May, 2017.
DOI: 10.1109/ccgrid.2017.141

Controlling the spread of infectious diseases in large populations is an important societal challenge. Mathematically, the problem is best captured as a certain class of reaction-diffusion processes (referred to as contagion processes) over appropriate synthesized interaction networks. Agent-based models have been successfully used in the recent past to study such contagion processes. We describe EpiSimdemics, a highly scalable, parallel code written in Charm++ that uses agent-based modeling to simulate disease spreads over large, realistic, co-evolving interaction networks. We present a new parallel implementation of EpiSimdemics that achieves unprecedented strong and weak scaling on different architectures — Blue Waters, Cori and Mira. EpiSimdemics achieves five times greater speedup than the second fastest parallel code in this field. This unprecedented scaling is an important step to support the long term vision of real-time epidemic science. Finally, we demonstrate the capabilities of EpiSimdemics by simulating the spread of influenza over a realistic synthetic social contact network spanning the continental United States (∼280 million nodes and 5.8 billion social contacts).

Progressive CPU Volume Rendering with Sample Accumulation
W. Usher, J. Amstutz, C. Brownlee, A. Knoll, I. Wald . In Eurographics Symposium on Parallel Graphics and Visualization, Edited by Alexandru Telea and Janine Bennett, The Eurographics Association, 2017.
ISBN: 978-3-03868-034-5
ISSN: 1727-348X
DOI: 10.2312/pgv.20171090

We present a new method for progressive volume rendering by accumulating object-space samples over successively rendered frames. Existing methods for progressive refinement either use image space methods or average pixels over frames, which can blur features or integrate incorrectly with respect to depth. Our approach stores samples along each ray, accumulates new samples each frame into a buffer, and progressively interleaves and integrates these samples. Though this process requires additional memory, it ensures interactivity and is well suited for CPU architectures with large memory and cache. This approach also extends well to distributed rendering in cluster environments. We implement this technique in Intel's open source OSPRay CPU ray tracing framework and demonstrate that it is particularly useful for rendering volumetric data with costly sampling functions.

Pathways for Theoretical Advances in Visualization
M. Chen, G. Grinstein, C. R. Johnson, J. Kennedy, M. Tory. In IEEE Computer Graphics and Applications, IEEE, pp. 103--112. July, 2017.

More than a decade ago, Chris Johnson proposed the "Theory of Visualization" as one of the top research problems in visualization. Since then, there have been several theory-focused events, including three workshops and three panels at IEEE Visualization (VIS) Conferences. Together, these events have produced a set of convincing arguments.

OpenMP 4 Fortran Modernization of WSM6 for KNL
T.A.J. Ouermi, A. Knoll, R.M. Kirby, M. Berzins. In Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact, PEARC17, No. 12, ACM, pp. 12:1--12:8. 2017.
ISBN: 978-1-4503-5272-7
DOI: 10.1145/3093338.3093387

Parallel code portability in the petascale era requires modifying existing codes to support new architectures with large core counts and SIMD vector units. OpenMP is a well established and increasingly supported vehicle for portable parallelization. As architectures mature and compiler OpenMP implementations evolve, best practices for code modernization change as well. In this paper, we examine the impact of newer OpenMP features (in particular OMP SIMD) on the Intel Xeon Phi Knights Landing (KNL) architecture, applied in optimizing loops in the single moment 6-class microphysics module (WSM6) in the US Navy's NEPTUNE code. We find that with functioning OMP SIMD constructs, low thread invocation overhead on KNL and reduced penalty for unaligned access compared to previous architectures, one can leverage OpenMP 4 to achieve reasonable scalability with relatively minor reorganization of a production physics code.

Improving Uintah's Scalability Through the Use of Portable Kokkos-Based Data Parallel Tasks
J. K. Holmen, A. Humphrey, D. Sutherland, M. Berzins. In Proceedings of the Practice and Experience in Advanced Research Computing 2017 on Sustainability, Success and Impact, PEARC17, No. 27, pp. 27:1--27:8. 2017.
ISBN: 978-1-4503-5272-7
DOI: 10.1145/3093338.3093388

The University of Utah's Carbon Capture Multidisciplinary Simulation Center (CCMSC) is using the Uintah Computational Framework to predict performance of a 1000 MWe ultra-supercritical clean coal boiler. The center aims to utilize the Intel Xeon Phi-based DOE systems, Theta and Aurora, through the Aurora Early Science Program by using the Kokkos C++ library to enable node-level performance portability. This paper describes infrastructure advancements and portability improvements made possible by our integration of Kokkos within Uintah. Scalability results are presented that compare serial and data parallel task execution models for a challenging radiative heat transfer calculation, central to the center's predictive boiler simulations. These results demonstrate both good strong-scaling characteristics to 256 Knights Landing (KNL) processors on the NSF Stampede system, and show the KNL-based calculation to compete with prior GPU-based results for the same calculation.

Department of Energy, Advanced Scientific Computing Advisory Committee (ASCAC), Subcommittee on LDRD Review Final Report
M. Berzins, D. A. Bonnell, Jr. Cizewski, K. M. Heeger, A.J.G. Hey, C. J. Keane, B. A. Ramsey, K. A. Remington, J.L. Rempe. May, 2017.

Using the material point method to model chemical/mechanical coupling in the deformation of a silicon anode
C. Gritton, J. Guilkey, J. Hooper, D. Bedrov, R. M. Kirby, M. Berzins. In Modelling and Simulation in Materials Science and Engineering, Vol. 25, No. 4, pp. 045005. 2017.

The lithiation and delithiation of a silicon battery anode is modeled using the material point method (MPM). The main challenges in modeling this process using the MPM is to simulate stress dependent diffusion coupled with concentration dependent stress within a material that undergoes large deformations. MPM is chosen as the numerical method of choice because of its ability to handle large deformations. A method for modeling diffusion within MPM is described. A stress dependent model for diffusivity and three different constitutive models that fully couple the equations for stress with the equations for diffusion are considered. Verifications tests for the accuracy of the numerical implementations of the models and validation tests with experimental results show the accuracy of the approach. The application of the fully coupled stress diffusion model implemented in MPM is applied to modeling the lithiation and delithiation of silicon nanopillars.

Evaluation of in-situ analysis strategies at scale for power efficiency and scalability
I. Rodero, M. Parashar, A.G. Landge, S. Kumar, V. Pascucci,, P.T. Bremer. In Cluster, Cloud and Grid Computing (CCGrid), 2016 16th IEEE/ACM International Symposium on, IEEE, pp. 156--164. 2016.

The increasing gap between available compute power and I/O capabilities is resulting in simulation pipelines running on leadership computing facilities being reformulated. In particular, in-situ processing is complementing conventional post-process analysis; however, it can be performed by using the same compute resources as the simulation or using secondary dedicated resources.

In this paper, we focus on three different in-situ analysis strategies, which use the same compute resources as the ongoing simulation but different data movement strategies. We evaluate the costs incurred by these strategies in terms of run time, scalability and power/energy consumption. Furthermore, we extrapolate power behavior to peta-scale and investigate different design choices through projections. Experimental evaluation at full machine scale on Titan supports that using fewer cores per node for in-situ analysis is the optimum choice in terms of scalability. Hence, further research effort should be devoted towards developing in-situ analysis techniques following this strategy in future high-end systems.

An Overview of Performance Portability in the Uintah Runtime System through the Use of Kokkos
D. Sunderland, B. Peterson, J. Schmidt, A. Humphrey, J. Thornock, M. Berzins. In 2016 Second International Workshop on Extreme Scale Programming Models and Middlewar (ESPM2), IEEE, Nov, 2016.
DOI: 10.1109/espm2.2016.012

The current diversity in nodal parallel computer architectures is seen in machines based upon multicore CPUs, GPUs and the Intel Xeon Phi's. A class of approaches for enabling scalability of complex applications on such architectures is based upon Asynchronous Many Task software architectures such as that in the Uintah framework used for the parallel solution of solid and fluid mechanics problems. Uintah has both an applications layer with its own programming model and a separate runtime system. While Uintah scales well today, it is necessary to address nodal performance portability in order for it to continue to do. Incrementally modifying Uintah to use the Kokkos performance portability library through prototyping experiments results in improved kernel performance by more than a factor of two.

In Situ Exploration of Particle Simulations with CPU Ray Tracing
W. Usher, I. Wald, A. Knoll, M. Papka, V. Pascucci. In Supercomputing Frontiers and Innovations, Vol. 3, No. 4, 2016.
ISSN: 2313-8734
DOI: 10.14529/jsfi160401

We present a system for interactive in situ visualization of large particle simulations, suitable for general CPU-based HPC architectures. As simulations grow in scale, in situ methods are needed to alleviate IO bottlenecks and visualize data at full spatio-temporal resolution. We use a lightweight loosely-coupled layer serving distributed data from the simulation to a data-parallel renderer running in separate processes. Leveraging the OSPRay ray tracing framework for visualization and balanced P-k-d trees, we can render simulation data in real-time, as they arrive, with negligible memory overhead. This flexible solution allows users to perform exploratory in situ visualization on the same computational resources as the simulation code, on dedicated visualization clusters or remote workstations, via a standalone rendering client that can be connected or disconnected as needed. We evaluate this system on simulations with up to 227M particles in the LAMMPS and Uintah computational frameworks, and show that our approach provides many of the advantages of tightly-coupled systems, with the flexibility to render on a wide variety of remote and co-processing resources.

VTK-m: Accelerating the Visualization Toolkit for Massively Threaded Architectures
K. Moreland, C. Sewell, W. Usher, L. Lo, J. Meredith, D. Pugmire, J. Kress, H. Schroots, K. Ma, H. Childs, M. Larsen, C. Chen, R. Maynard, B. Geveci. In IEEE Computer Graphics and Applications, Vol. 36, No. 3, pp. 48--58. May, 2016.
ISSN: 0272-1716
DOI: 10.1109/MCG.2016.48

Traditional scientific visualization software approaches do not fare well in massively threaded environments. To address the needs of the high-performance computing community, the VTK-m framework fills the gaps in functionality by bringing together the most recent research.

Improving accuracy in the MPM method using a null space filter
C. Gritton, M. Berzins. In Computational Particle Mechanics, pp. 1--12. 2016.
ISSN: 2196-4386
DOI: 10.1007/s40571-016-0134-3

The material point method (MPM) has been very successful in providing solutions to many challenging problems involving large deformations. Nevertheless there are some important issues that remain to be resolved with regard to its analysis. One key challenge applies to both MPM and particle-in-cell (PIC) methods and arises from the difference between the number of particles and the number of the nodal grid points to which the particles are mapped. This difference between the number of particles and the number of grid points gives rise to a non-trivial null space of the linear operator that maps particle values onto nodal grid point values. In other words, there are non-zero particle values that when mapped to the grid point nodes result in a zero value there. Moreover, when the nodal values at the grid points are mapped back to particles, part of those particle values may be in that same null space. Given positive mapping weights from particles to nodes such null space values are oscillatory in nature. While this problem has been observed almost since the beginning of PIC methods there are still elements of it that are problematical today as well as methods that transcend it. The null space may be viewed as being connected to the ringing instability identified by Brackbill for PIC methods. It will be shown that it is possible to remove these null space values from the solution using a null space filter. This filter improves the accuracy of the MPM methods using an approach that is based upon a local singular value decomposition (SVD) calculation. This local SVD approach is compared against the global SVD approach previously considered by the authors and to a recent MPM method by Zhang and colleagues.

An Overview of Performance Portability in the Uintah Runtime System Through the Use of Kokkos
D. Sunderland, B. Peterson, J. Schmidt, A. Humphrey, J. Thornock,, M. Berzins. In Proceedings of the Second Internationsl Workshop on Extreme Scale Programming Models and Middleware, Salt Lake City, Utah, ESPM2, IEEE Press, Piscataway, NJ, USA pp. 44--47. 2016.
ISBN: 978-1-5090-3858-9
DOI: 10.1109/ESPM2.2016.10

Packing Configurations of PBX-9501 Cylinders to Reduce the Probability of a Deflagration to Detonation Transition (DDT)
J. Beckvermit, T. Harman, C. Wight,, M. Berzins. In Propellants, Explosives, Pyrotechnics, 2016.
ISSN: 1521-4087
DOI: 10.1002/prep.201500331

The detonation of hundreds of explosive devices from either a transportation or storage accident is an extremely dangerous event. This paper focuses on identifying ways of packing/storing arrays of explosive cylinders that will reduce the probability of a Deflagration to Detonation Transition (DDT). The Uintah Computational Framework was utilized to predict the conditions necessary for a large scale DDT to occur. The results showed that the arrangement of the explosive cylinders and the number of devices packed in a "box" greatly effects the probability of a detonation.

Special Section on Two Themes: CSE Software and Big Data in CSE
H. De Sterck, C. Johnson,, L. C. McInnes. In SIAM J. Sci. Comput, Vol. 38, No. 5, SIAM, pp. S1--S2. 2016.

The 2015 SIAM Conference on Computational Science and Engineering (CSE) was held March 14-18, 2015, in Salt Lake City, Utah. The SIAM Journal on Scientific Computing (SISC) created this special section in association with the CSE15 conference. The special section focuses on two topics that are of significant current interest to CSE researchers: CSE software and big data in CSE.

Read More: http://epubs.siam.org/doi/abs/10.1137/16N974188

Research and Education in Computational Science and Engineering
Subtitled “Report from a workshop sponsored by the Society for Industrial and Applied Mathematics (SIAM) and the European Exascale Software Initiative (EESI-2),” U. Rüde, K. Willcox, L. C. McInnes, H. De Sterck, G. Biros, H. Bungartz, J. Corones, E. Cramer, J. Crowley, O. Ghattas, M. Gunzburger, M. Hanke, R. Harrison, M. Heroux, J. Hesthaven, P. Jimack, C. Johnson, K. E. Jordan, D. E. Keyes, R. Krause, V. Kumar, S. Mayer, J. Meza, K. M. Mørken, J. T. Oden, L. Petzold, P. Raghavan, S. M. Shontz, A. Trefethen, P. Turner, V. Voevodin, B. Wohlmuth, C. S. Woodward. Aug, 2016.

Over the past two decades the field of computational science and engineering (CSE) has penetrated both basic and applied research in academia, industry, and laboratories to advance discovery, optimize systems, support decision-makers, and educate the scientific and engineering workforce. Informed by centuries of theory and experiment, CSE performs computational experiments to answer questions that neither theory nor experiment alone is equipped to answer. CSE provides scientists and engineers of all persuasions with algorithmic inventions and software systems that transcend disciplines and scales. Carried on a wave of digital technology, CSE brings the power of parallelism to bear on troves of data. Mathematics-based advanced computing has become a prevalent means of discovery and innovation in essentially all areas of science, engineering, technology, and society; and the CSE community is at the core of this transformation. However, a combination of disruptive developments---including the architectural complexity of extreme-scale computing, the data revolution that engulfs the planet, and the specialization required to follow the applications to new frontiers---is redefining the scope and reach of the CSE endeavor. This report describes the rapid expansion of CSE and the challenges to sustaining its bold advances. The report also presents strategies and directions for CSE research and education for the next decade.

Page 6 of 22

Start
Prev
1
2
3
4
5
6
7
8
9
10
Next
End

SCI