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Flexible Data Fusion for Air Quality Estimation and Forecasting in Google Earth Engine to support Global Health Management Needs

The assessment and forecasting of air quality around the world at high spatial and temporal resolution can be enhanced by integrating data from multiple sources including models, satellites, regulatory monitors, and low-cost sensors. Such integration is subject to numerous technical challenges, however, including heterogeneous data resolution and formatting, different levels of data availability and reliability, and computational and capacity challenges to developing data fusion tools and platforms. This presentation will provide an overview of a NASA-funded effort to develop a data fusion system within the Google Earth Engine platform which integrates these air quality data sources to produce comprehensive assessments and forecasts of key air pollutants at sub-daily and sub-city scales. The system is being developed in collaboration with city- and regional-level air quality managers and will provide them with information to the assess and anticipate the health impacts of poor air quality, track local changes in air quality due to ongoing transportation and land use changes, and identify potential gaps in their current air quality monitoring strategies. The presentation will report advances achieved through the project, including bringing local air quality monitoring data into Google Earth Engine, quantifying uncertainties in air quality estimates and forecasts, and tailored communications tools providing integration into end-user processes to meet their needs.

Nathan R. Pavlovic↗

DOE OSTI2023

ExaWind at NREL: Upping the Ante

The objective of the ExaWind component of the Exascale Computing Project is to deliver many-turbine blade-resolved simulations in complex terrain. These simulations bring new challenges to both compute and analysis of the resulting data. In this paper/video, we visually explore the impact of ExaWind on wind simulations through two studies of a small wind farm under two atmospheric conditions. We then turn to analysis and review tools that visualization researchers at NREL use to answer the challenges that ExaWind brings.

collaborative visualization↗

DOE OSTI2020

Towards Enabling Predictive Scale-Bridging Simulations through Active Learning [Slides]

Topics in this presentation include: Overview of the project, Active Learning approach, Mixing and transport in Inertial Confinement Fusion simulations, Computer science infrastructure challenges, and Preliminary numerical results.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

An expanded evaluation of protein function prediction methods shows an improvement in accuracy

Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2016

Toward Quantitatively Accurate Calculation of the Redox-Associated Acid–Base and Ligand Binding Equilibria of Aquacobalamin

In redox processes in complex transition metal-containing species are often intimately associated with changes in ligand protonation states and metal coordination number. Moreover, a major challenge is therefore to develop consistent computational approaches for computing pH-dependent redox and ligand dissociation properties of organometallic species. Reduction of the Co center in the vitamin B12 derivative aquacobalamin can be accompanied by ligand dissociation, protonation, or both, making these properties difficult to compute accurately. We examine this challenge here by using density functional theory and continuum solvation to compute Co ligand binding equilibrium constants (Kon/off), pKas and reduction potentials for models of aquacobalamin in aqueous solution. We consider two models for cobalamin ligand coordination: the first follows the hexa, penta, tetra coordination scheme for Co III , Co II , and Co I species, respectively, and the second model features saturation of each vacant axial coordination site on Co II and Co I species with a single, explicit water molecule to maintain six directly interacting ligands or water molecules in each oxidation state. Comparing these two coordination schemes in combination with five dispersion-corrected density functionals, we find that the accuracy of the computed properties is largely independent of the scheme used, but including only a continuum representation of the solvent yields marginally better results than saturating the first solvation shell around Co throughout. PBE performs best, displaying balanced accuracy and superior performance overall, with RMS errors of 80 mV for seven reduction potentials, 2.0 log units for five pK a s and 2.3 log units for two log K on/off values for the aquacobalamin system. Furthermore, we find that the BP86 functional commonly used in corrinoid studies suffers from erratic behavior and inaccurate descriptions of Co axial ligand binding, leading to substantial errors in predicted pK a s and K on/off values. Finally, these findings demonstrate the effectiveness of the present approach for computing electrochemical and thermodynamic properties of a complex transition metal-containing cofactor.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Accelerating catalytic advancements through the precision of high-throughput experiments & calculations

The growing demand for energy-efficient processes to support a sustainable future drives the need for research to rapidly explore chemical and material space through accelerated catalyst discovery initiatives. Recent breakthroughs in high-throughput experimental and computational methods are transforming the catalysis field, surpassing traditional approaches to manipulating variables in catalytic processes. Key advancements in innovation include the integration of machine learning for efficient catalyst screening, high-throughput experimentation, data-driven methodologies employing comprehensive databases, and in situ and in operando techniques for realistic observations. This progress has undoubtedly been intertwined with a collaborative framework across disciplines, reshaping catalyst discovery methods in both industry and academia. This Opinion article presents a multifaceted perspective from coauthors with expertise spanning various stages of the Technology Readiness Level spectrum, highlighting both opportunities and persistent challenges in integrating computational and experimental approaches in catalysis. These challenges span from obtaining high-quality experimental data, scaling simulations to industrially relevant materials and process conditions to navigating the complexity and predictive accuracy of computational models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Computational Strategies for Scalable Genomics Analysis

The revolution in next-generation DNA sequencing technologies is leading to explosive data growth in genomics, posing a significant challenge to the computing infrastructure and software algorithms for genomics analysis. Various big data technologies have been explored to scale up/out current bioinformatics solutions to mine the big genomics data. In this review, we survey some of these exciting developments in the applications of parallel distributed computing and special hardware to genomics. We comment on the pros and cons of each strategy in the context of ease of development, robustness, scalability, and efficiency. Although this review is written for an audience from the genomics and bioinformatics fields, it may also be informative for the audience of computer science with interests in genomics applications.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Graph Contractions for Calculating Correlation Functions in Lattice QCD

Computing correlation functions for many-particle systems in Lattice QCD is vital to extract nuclear physics observables like the energy spectrum of hadrons such as protons. However, this type of calculation has long been considered to be very challenging and computing-resource intensive because of the complex nature of a hadron composed of quarks with many degrees of freedom. In particular, a correlation function can be calculated through a sum of all possible pairs of quark contractions, each of which is a batched tensor contraction, dictated by Wick's theorem. Because the number of terms of this sum can be very large for any hadronic system of interest, fast evaluation of the sum faces several challenges: an extremely large number of contractions, a huge memory footprint at runtime, and the speed of tensor contractions. In this paper, we present a Lattice QCD analysis software suite, Redstar, which addresses these challenges by utilizing novel algorithmic and software engineering methods targeting modern computing platforms such as many-core CPUs and GPUs. In particular, Redstar represents every term in the sum of a correlation function by a graph, applies efficient graph algorithms to reduce the number of contractions to lower the cost of computations, and minimizes the total memory footprint. Moreover, Redstar carries out the contractions on either CPUs or GPUs utilizing an internal and highly efficient Hadron contraction library. Specifically, we illustrate some important algorithmic optimizations of Redstar, show various key design features of Hadron library, and present the speedup values due to the optimizations along with performance figures for calculating six correlations functions on four computing platforms.

Chen, Jie↗

DOE OSTI2023

Graph Contractions for Calculating Correlation Functions in Lattice QCD

Chen, Jie↗

DOE OSTI2017

Corrections to nucleon capture cross sections computed in truncated Hilbert spaces

Nucleon capture cross sections enter various astrophysical processes. Here, the measurement of proton capture on nuclei at astrophysically relevant low energies is a challenge, and theoretical computations in this long-wavelength regime are sensitive to the long-distance asymptotics of the wave functions. A theoretical foundation for estimating and correcting errors introduced in capture cross sections due to Hilbert space truncation has so far been lacking. We derive extrapolation formulas that relate the infrared regularized capture amplitudes to the infinite basis limit and demonstrate their efficacy for proton-proton fusion. Our results are thus relevant to current calculations of few-body capture reactions such as proton-proton fusion or proton capture on the deuteron, and they also open the way for the use of ab initio many-body wave functions represented in finite Hilbert spaces in precision calculations of nucleon capture on heavier nuclei.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2013

GPU-accelerated Software Library for Unsteady Flamelet Modeling of Turbulent Combustion with Complex Chemical Kinetics

Modeling and simulation of turbulent combustion are computationally demanding and challenging problems due to the complexity of turbulent flows and the complex chemical reaction kinetics. Recent advances in high performance computing (HPC) has allowed simulations to utilize increasing computer power to obtain newer insights that were not feasible before. The arrival of GPUs and many-core processors is set to revolutionize the HPC landscape. This paper presents an unsteady flamelet software library developed for utilizing GPUs as an onboard accelerator while the flow solver continues to run on conventional host processors using legacy software. The software was architected to expose multiple levels of parallelism and concurrency available in the unsteady flamelet model which is also necessary to realize the full potential of these accelerators. While the model and its coupling with the flow solver have been known for some time, the potential to target these models and libraries for newer HPC architectures provides a greater incentive for pursuing combustion model development.

Sankaran, Ramanan↗

DOE OSTI2022

Assessing the stability of noisy quantum computation

Quantum computation has made considerable progress in the last decade with multiple emerging technologies providing proof-of-principle experimental demonstrations of such calculations. However, these experimental demonstrations of quantum computation face technical challenges due to the noise and errors that arise from imperfect implementation of the technology. Here, we frame the concepts of computational accuracy, result reproducibility, device reliability and program stability in the context of quantum computation. We provide intuitive definitions for these concepts in the context of quantum computation that lead to operationally meaningful bounds on program output. Our assessment highlights the continuing need for statistical analyses of quantum computing program to increase our confidence in the burgeoning field of quantum information science.

Dasgupta, Samudra↗

DOE OSTI2023

Efficient GPU Implementation of Automatic Differentiation for Computational Fluid Dynamics

Many scientific and engineering applications require repeated calculations of derivatives of output functions with respect to input parameters. Automatic Differentiation (AD) is a method that automates derivative calculations and can significantly speed up code development. In Computational Fluid Dynamics (CFD), derivatives of flux functions with respect to state variables (Jacobian) are needed for efficient solutions of the nonlinear governing equations. AD of flux functions on graphics processing units (GPUs) is challenging as flux computations involve many intermediate variables that create high register pressure and require significant memory traffic because of the need to store the derivatives. This paper presents a forward-mode AD method based on multivariate dual numbers that addresses these challenges and simultaneously reduces the floating-point operation count. The dimension of the multivariate dual numbers is optimized for performance. The flux computations are restructured to minimize the number of temporary variables and reduce register pressure. For effective utilization of memory bandwidth, shared memory is used to store the local flux Jacobian. This AD implementation is compared with several other Jacobian implementations on an NVIDIA V100 GPU (V100). For three-dimensional perfect-gas compressible-flow equations implemented in a practical CFD code, the AD implementation of a flux Jacobian based on multivariate dual numbers of dimension 5 outperforms all other GPU AD implementations on V100. Its performance is comparable with the optimized hand-differentiated version. Finally, the implementation achieves 75% of the peak floating-point throughput and 61 % of the peak global device memory bandwidth usage.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2016

Software Systems for High-performance Quantum Computing

Quantum computing promises new opportunities for solving hard computational problems, but harnessing this novelty requires breakthrough concepts in the design, operation, and application of computing systems. We define some of the challenges facing the development of quantum computing systems as well as software-based approaches that can be used to overcome these challenges. Following a brief overview of the state of the art, we present models for the quantum programming and execution models, the development of architectures for hybrid high-performance computing systems, and the realization of software stacks for quantum networking. This leads to a discussion of the role that conventional computing plays in the quantum paradigm and how some of the current challenges for exascale computing overlap with those facing quantum computing.

high-performance computing↗

NASA NTRS1998

The Design and Evaluation of "CAPTools"--A Computer Aided Parallelization Toolkit

Writing applications for high performance computers is a challenging task. Although writing code by hand still offers the best performance, it is extremely costly and often not very portable. The Computer Aided Parallelization Tools (CAPTools) are a toolkit designed to help automate the mapping of sequential FORTRAN scientific applications onto multiprocessors. CAPTools consists of the following major components: an inter-procedural dependence analysis module that incorporates user knowledge; a 'self-propagating' data partitioning module driven via user guidance; an execution control mask generation and optimization module for the user to fine tune parallel processing of individual partitions; a program transformation/restructuring facility for source code clean up and optimization; a set of browsers through which the user interacts with CAPTools at each stage of the parallelization process; and a code generator supporting multiple programming paradigms on various multiprocessors. Besides describing the rationale behind the architecture of CAPTools, the parallelization process is illustrated via case studies involving structured and unstructured meshes. The programming process and the performance of the generated parallel programs are compared against other programming alternatives based on the NAS Parallel Benchmarks, ARC3D and other scientific applications. Based on these results, a discussion on the feasibility of constructing architectural independent parallel applications is presented.

Yan, Jerry↗

DOE OSTI2023

HPC-driven computational reproducibility in numerical relativity codes: a use case study with IllinoisGRMHD

Abstract Reproducibility of results is a cornerstone of the scientific method. Scientific computing encounters two challenges when aiming for this goal. Firstly, reproducibility should not depend on details of the runtime environment, such as the compiler version or computing environment, so results are verifiable by third-parties. Secondly, different versions of software code executed in the same runtime environment should produceconsistent numerical results for physical quantities. In this manuscript, we test the feasibility of reproducing scientific results obtained using theIllinoisGRMHDcode that is part of an open-source community software for simulation in relativistic astrophysics, theEinstein Toolkit. We verify that numerical results of simulating a single isolated neutron star withIllinoisGRMHDcan be reproduced, and compare them to results reported by the code authors in 2015. We use two different supercomputers: Expanse at SDSC, and Stampede2 at TACC. By compiling the source code archived along with the paper on both Expanse and Stampede2, we find thatIllinoisGRMHDreproduces results published in its announcement paper up to errors comparable to round-off level changes in initial data parameters. We also verify that a current version ofIllinoisGRMHDreproduces these results once we account for bug fixes which have occurred since the original publication.

Astronomy & Astrophysics↗

DOE OSTI2014

Analysis of Shutdown Dose Rate in Fusion Energy Systems Using Hybrid Monte Carlo/Deterministic Techniques - Paper 138

Accurate assessments of shutdown dose rate (SDDR) are critical to support operation, maintenance, and waste disposal planning and to guide possible design changes of critical components in fusion energy systems. An SDDR calculation involves three steps: 1. a neutron transport calculation to determine the space and energy neutron flux distributions, 2. an activation calculation for computing the photon source distribution, and 3. a photon transport calculation for the estimation of the final SDDR. A companion paper describes the challenges of SDDR computations in fusion energy systems and the status of the techniques that are currently being used in SDDR analysis. Of these techniques, the rigorous 2-step (R2S) computational system entails Monte Carlo (MC) neutron and photon transport calculations coupled with a comprehensive activation step using a dedicated inventory code and library. The use of global MC variance reduction techniques was suggested for accelerating the SDDR MC neutron transport calculation. These techniques, which attempt to calculate MC tallies with nearly uniform relative uncertainties in both the low-flux space-energy regions and the high-flux space-energy regions, do not preferentially focus the MC computational efforts toward space-energy regions of high importance to the final decay dose. The ability of these approaches to accurately predict SDDR is inhibited by their prohibitive computational costs, which will be on the order of thousands of processor-years for full-scale modeling of an entire fusion plant. The companion paper describes the theoretical background of the Multi-Step Consistent Adjoint Driven Importance Sampling (MS-CADIS) method, which has been proposed to speed up SDDR MC neutron calculations. The MS-CADIS method uses the CADIS method, which has been successfully used for more than a decade in shielding calculations, but focuses on multistep shielding calculations such as SDDR analyses. The companion paper also describes a new method for calculating the uncertainties in SDDR due to uncertainties in the MC neutron calculation. This new method of uncertainty propagation uses the MS-CADIS neutron adjoint source to propagate the uncertainties in the neutron fluxes to the SDDR. This paper describes the application of the MS-CADIS method for speeding up the SDDR MC calculations and for calculating the SDDR uncertainty due to the neutron flux uncertainties. A new metric for assessing the reliability of SDDR calculations was also suggested and used in this analysis. The ITER benchmark problem was used in this analysis. The problem resembles the configuration and geometrical arrangement of an upper port plug in ITER. The analysis compared the efficiency of the MS-CADIS method to the traditional approach of using global MC variance reduction techniques for speeding up SDDR MC neutron calculation. The application of the MS-CADIS method to SDDR calculations in fusion energy systems was tested using the ITER benchmark problem. Compared to the standard FW-CADIS method, the increase in the efficiency of the SDDR neutron MC calculation due to the use of the MS-CADIS method was between 18% and 69%. The MS-CADIS method also increases the fraction of non-zero-scoring mesh tally elements in the space-energy regions of high importance to the final SDDR. Implementation of the MS-CADIS method in the SCALE and the ADVANTG code systems is currently under way. (authors)

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2025

Nonlinear simulation of under-resolved flows with shocks

Here, we consider the numerical simulation of advection-dominated flows whose wide range of physical length scales exceed the memory capacity of finite computers. Simulating flows with shocks and turbulence presented challenges for the earliest computers that were quickly overcome by the development of new numerical methodology. Principal among those new ideas were artificial viscosity and finite volume methods, concepts that remain in common use today. We begin by describing the history of those methods, the innovators and their motivations. We then describe the development of finite scale theory, a reformulation of Navier–Stokes theory that exposes the physical principles on which artificial viscosity is based. We discuss the essential properties of the finite scale equations, the observer, unresolved kinetic energy and inviscid energy dissipation. We briefly consider the implementation of the finite scale equations on the computer from the point of view of Gisin’s conjectures about finite information.

97 MATHEMATICS AND COMPUTING↗