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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Advances in Mixed Precision Algorithms: 2021 Edition

Over the last year, the ECP xSDK-multiprecision effort has made tremendous progress in developing and deploying new mixed precision technology and customizing the algorithms for the hardware deployed in the ECP flagship supercomputers. The effort also has succeeded in creating a cross-laboratory community of scientists interested in mixed precision technology and now working together in deploying this technology for ECP applications. In this report, we highlight some of the most promising and impactful achievements of the last year. Among the highlights we present are: Mixed precision IR using a dense LU factorization and achieving a 1.8× speedup on Spock; results and strategies for mixed precision IR using a sparse LU factorization; a mixed precision eigenvalue solver; Mixed Precision GMRES-IR being deployed in Trilinos, and achieving a speedup of 1.4× over standard GMRES; compressed Basis (CB) GMRES being deployed in Ginkgo and achieving an average 1.4× speedup over standard GMRES; preparing hypre for mixed precision execution; mixed precision sparse approximate inverse preconditioners achieving an average speedup of 1.2×; and detailed description of the memory accessor separating the arithmetic precision from the memory precision, and enabling memory-bound low precision BLAS 1/2 operations to increase the accuracy by using high precision in the computations without degrading the performance. We emphasize that many of the highlights presented here have also been submitted to peer-reviewed journals or established conferences, and are under peer-review or have already been published.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Advances in Mixed Precision Algorithms: 2021 Edition

Over the last year, the ECP xSDK-multiprecision effort has made tremendous progress in developing and deploying new mixed precision technology and customizing the algorithms for the hardware deployed in the ECP flagship supercomputers. The effort also has succeeded in creating a cross-laboratory community of scientists interested in mixed precision technology and now working together in deploying this technology for ECP applications. In this report, we highlight some of the most promising and impactful achievements of the last year. Among the highlights we present are • Mixed precision IR using a dense LU factorization and achieving a 1.8× speedup on Spock; • Results and strategies for mixed precision IR using a sparse LU factorization; • A mixed precision eigenvalue solver; • Mixed Precision GMRES-IR being deployed in Trilinos, and achieving a speedup of 1.4× over standard GMRES; • Compressed Basis (CB) GMRES being deployed in Ginkgo and achieving an average 1.4× speedup over standard GMRES; • Preparing hypre for mixed precision execution; • Mixed precision sparse approximate inverse preconditioners achieving an average speedup of 1.2×; • Detailed description of the memory accessor separating the arithmetic precision from the memory precision, and enabling memory-bound low precision BLAS 1/2 operations to increase the accuracy by using high precision in the computations without degrading the performance. We emphasize that many of the highlights presented here have also been submitted to peer-reviewed journals or established conferences, and are under peer-review or have already been published.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Quantitative Non-Destructive Evaluation of Fatigue Damage Based on Multi-Sensor Fusion

Based on sensor fusion and machine learning, this project developed a novel non-destructive evaluation (NDE) methodology, which consists of a remaining useful life (RUL) prediction framework and regression models for predicting residual stress and full width at half maximum (FWHM). A series of fatigue testing experiments were conducted using 5052-H32 aluminum alloy specimens. All specimens were measured using linear ultrasonic (LU) and nonlinear ultrasonic (NLU) testing methods non-destructively. Machine learning models were developed to use LU and NLU measurements to predict loading condition, fatigue level, residual stress, and FWHM. It was demonstrated that the developed methodology could distinguish new and fatigue specimens with an accuracy of 97.53%. Also, the prediction errors for residual stress and FWHM were as low as 4.73% and 1.62%, respectively. An interactive database was created to publicly share the data generated from the project. It is envisioned that the developed NDE technology will equip manufacturers with a responsive screening system for incoming used metallic components, and potentially lead to a significant increase in using used metallic components for remanufacturing.

42 ENGINEERING↗
DOE OSTI2020

Global land use for 2015-2100 at 0.05° resolution under diverse socioeconomic and climate scenarios

Global future land use (LU) is an important input for Earth system models for projecting Earth system dynamics and is critical for many modeling studies on future global change. Here we generated a new global gridded LU dataset using the Global Change Analysis Model (GCAM) and a land use spatial downscaling model, named Demeter, under the five Shared Socioeconomic Pathways (SSPs) and four Representative Concentration Pathways (RCPs) scenarios. Compared to existing similar datasets, the presented dataset has a higher spatial resolution (0.05°×0.05°) and spreads under a more comprehensive set of SSP-RCP scenarios (in total 15 scenarios), and considers uncertainties from the forcing climates. We compared our dataset with the Land Use Harmonization version 2 (LUH2) dataset and found our results are in general spatially consistent with LUH2. The presented dataset will be useful for global Earth system modeling studies, especially for the analysis of the impacts of land use and land cover change and socioeconomics, as well as the characterizing the uncertainties associated with these impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

225 Ac-MACROPATATE: A Novel α-Particle Peptide Receptor Radionuclide Therapy for Neuroendocrine Tumors

Neuroendocrine tumors (NETs) express somatostatin receptors (SSTRs) 2 and 5. Modified variants of somatostatin, the cognate ligand for SSTR2 and SSTR5, are used in treatment for metastatic and locoregional disease. Peptide receptor radionuclide therapy with 177 Lu-DOTATATE (DOTA-octreotate), a β-particle–emitting somatostatin derivative, has demonstrated survival benefit in patients with SSTR-positive NETs. Despite excellent results, a subset of patients has tumors that are resistant to treatment, and alternative agents are needed. Targeted α-particle therapy has been shown to kill tumors that are resistant to targeted β-particle therapy, suggesting that targeted α-particle therapy may offer a promising treatment option for patients with 177 Lu-DOTATATE–resistant disease. Although DOTATATE can chelate the clinically relevant α-particle–emitting radionuclide 225 Ac, the labeling reaction requires high temperatures, and the resulting radioconjugate has suboptimal stability. Methods: We designed and synthesized MACROPATATE (MACROPA-octreotate), a novel radioconjugate capable of chelating 225 Ac at room temperature, and assessed its in vitro and in vivo performance. Results: MACROPATATE demonstrated comparable affinity to DOTATATE (dissociation constant, 21 nM) in U2-OS-SSTR2, a SSTR2-positive transfected cell line. 225 Ac-MACROPATATE demonstrated superior serum stability at 37°C over time compared with 225 Ac-DOTATATE. Biodistribution studies demonstrated higher tumor uptake of 225 Ac-MACROPATATE than of 225 Ac-DOTATATE in mice engrafted with subcutaneous H69 NETs. Therapy studies showed that 225 Ac-MACROPATATE exhibits significant antitumor and survival benefit compared with saline control in mice engrafted with SSTR-positive tumors. However, the increased accumulation of 225 Ac-MACROPATATE in liver and kidneys and subsequent toxicity to these organs decreased its therapeutic index compared with 225 Ac-DOTATATE. Conclusion: 225 Ac-MACROPATATE and 225 Ac-DOTATATE exhibit favorable therapeutic efficacy in animal models. Because of elevated liver and kidney accumulation and lower administered activity for dose-limiting toxicity of 225 Ac-MACROPATATE, 225 Ac-DOTATATE was deemed the superior agent for targeted α-particle peptide receptor radionuclide therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

Strongly Correlated Quantum Spin Liquids versus Heavy Fermion Metals: A Review

This review considers the topological fermion condensation quantum phase transition (FCQPT) that explains the complex behavior of strongly correlated Fermi systems, such as frustrated insulators with quantum spin liquid and heavy fermion metals. The review contrasts theoretical consideration with recent experimental data collected on both heavy fermion metals (HF) and frustrated insulators. Such a method allows to understand experimental data. We also consider experimental data collected on quantum spin liquid in Lu 3 Cu 2 Sb 3 O 14 and quasi-one dimensional (1D) quantum spin liquid in both YbAlO 3 and Cu(C 4 H 4 N 2 )(NO 3 ) 2 with the aim to establish a sound theoretical explanation for the observed scaling laws, Landau Fermi liquid (LFL) and non-Fermi-liquid (NFL) behavior exhibited by these frustrated insulators. The recent experimental data on the heavy-fermion metal α-YbAl 1-x Fe x B 4 , with x=0.014, and on its sister compounds β-YbAlB 4 and YbCo 2 Ge 4 , carried out under the application of magnetic field as a control parameter are analyzed. We show that the thermodynamic and transport properties as well as the empirical scaling laws follow from the fermion condensation theory. We explain how both the similarity and the difference in the thermodynamic and transport properties of α-YbAl 1-x Fe x B 4 and in its sister compounds β-YbAlB 4 and YbCo 2 Ge 4 emerge, as well as establish connection of these (HF) metals with insulators Lu 3 Cu 2 Sb 3 O 14 , Cu(C 4 H 4 N 2 )(NO 3 ) 2 and YbAlO 3 . We demonstrate that the universal LFL and NFL behavior emerge because the HF compounds and the frustrated insulators are located near the topological FCQPT or are driven by the application of magnetic fields.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Investigation of the 176 Yb Interference Correction during Determination of the 176 Hf/ 177 Hf Ratio by Laser Ablation and Solution Analysis on the Neoma MC-ICP-MS

We utilized the Neoma™, a recently released MC-ICP-MS platform offered by ThermoFisher Scientific, to assess the behavior of the Lu-Yb-Hf system during laser ablation analyses of various zircon standards as well as solution-based analyses of the JMC-475 Hf standard doped with varying quantities of Yb and Lu. The primary goal of this work was to characterize the behavior of the Yb interference correction on the Neoma™ platform since this is one of the biggest issues in the Hf isotope analysis community and because the Neoma™ platform will supplant the Neptune™ series instrument. During laser ablation analysis, we found that the overall data quality scales proportionally with the total Hf signal intensity, with higher signal analyses producing extremely accurate (within 1 ε Hf unit) and precise (sub ε Hf unit within-run standard errors) data. At low Yb signals (<0.1 V 173 Yb), we were not able to produce an accurate internal Yb mass bias factor. However, utilizing an empirical approach allows for the application of session-specific relationships between the Yb and Hf mass bias factors, determined by analysis of standards of varying Yb content, to produce accurate ε Hf values from zircons with higher Yb/Hf ratios even where the total Hf signal intensity is relatively low. Similar behavior was observed in the solution analyses. Lastly, while the behavior of the Yb interference correction on the Neoma™ platform appears comparable to the Neptune™ series MC-ICP-MS, further work will help refine the understanding of the controls on mass bias behavior, oxide formation, session-to-session stability, etc.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Studying the hadron structure with PANDA and CLAS using machine learning techniques

The hadron spectroscopy and structure are currently very active fields of research to study the non-perturbative regime of quantum chronodynamics. The first one studies the complex structure of excited hadrons by looking at their decay products, while the latter uses lepton scattering on nucleons. Both methods require reconstruction algorithms with great efficiency and good particle identification and background rejection rates. This work aims to provide these by either improving the existing methods or developing new ones. The first part of this document presents a feasibility study of a predicted hybrid charmonium state for the PANDA experiment. Lattice QCD calculations predict the ground state hybrid charmonium to be a spin exotic with quantum numbers of JP C = 1?+ at a mass of around 4.3 GeV with a width to be around 20 MeV. A machine learning based data analysis scheme is proposed to further improve the signal efficiency and the background reduction, alongside with improvements of the analysis software (PandaRoot), that are vital for this study. These improvements include a reworked clustering algorithm for the electromagnetic calorimeter (EMC) and an optimized monte carlo matching for neutral particles. The second part of this document is about studying the proton structure. A multidimensional study of the structure function ratio Fsin(?)LU /FUU has been performed for K±, based on the measurement of beam-spin asymmetries. It uses the high statistics data recorded with the CLAS12 spectrometer at Jefferson Laboratory. Fsin(?)LU is a twist-3 quantity that provides information about the quark gluon correlations in the proton. This document will present for the first time a simultaneous analysis of two kaon channels over a large kinematic range of z, xB , PT and Q2 with virtualities Q2 ranging from 1 GeV2 up to 8 GeV2 using machine learning techniques for improved particle identification.

Kripko, Aron↗
DOE OSTI2023

Measuring the Beam-Spin Asymmetry of Hard Exclusive ¿0 Production off the Neutron with CLAS12

Over the course of the last couple of decades, Generalised Parton Distributions (GPDs) have become a compelling area of focus in both theoretical and experimental research in the ?eld of hadronic structure. GPDs can be indirectly accessed via observables in hard exclusive reactions such as Deeply Virtual Compton Scattering (DVCS) and Deeply Virtual Meson Production (DVMP). In these reactions, an incident lepton scatters o? an individual quark within the target nucleon via the exchange of a virtual photon, and a real photon (DVCS) or a meson (DVMP) are emitted as a result. This thesis presents an analysis of Deeply Virtual ?0 Meson Production o? the neutron in the deuteron (nDV?0P). The data were taken with the CLAS12 detector, which is housed in the experimental Hall B at the Je?erson National Laboratory (JLab) in Virginia, USA. A longitudinally polarised electron beam delivered by JLab?s Continuous Electron Beam accelerator Facility (CEBAF), at an energy of 10.2 and 10.6 GeV, was incident on a liquid deuterium target housed within the CLAS12 detector assembly. The nDV?0P channel is sensitive to the lesser-studied chiral-odd transversity GPDs. GPDs are accessed indirectly via observables related to the cross-sections of hard exclusive processes. One such example is the beam-spin asymmetry (?LU) as a function of Trento-?, the angle between the leptonic plane and the hadronic plane which are de?ned with the beam and scattered electron, and with the recoiling neutron and reconstructed ?0, respectively. Despite the limited statistics, the observable ?LU was successfully extracted for eight kinematic bins (four in Mandelstam ?, and two in ??). This is a ?rst measurement for nDV?0P in the phase-space which is now available with CLAS12 in the 12 GeV era of JLab.

Naidoo, Paul↗
DOE OSTI2022

A comprehensive and fair comparison of two neural operators (with practical extensions) based on $\mathrm{FAIR}$ data

Neural operators can learn nonlinear mappings between function spaces and offer a new simulation paradigm for real-time prediction of complex dynamics for realistic diverse applications as well as for system identification in science and engineering. Herein, we investigate the performance of two neural operators, which have shown promising results so far, and we develop new practical extensions that will make them more accurate and robust and importantly more suitable for industrial-complexity applications. The first neural operator, DeepONet, was published in 2019 (Lu et al., 2019), and its original architecture was based on the universal approximation theorem of Chen & Chen (1995). The second one, named Fourier Neural Operator or FNO, was published in 2020, and it is based on parameterizing the integral kernel in the Fourier space. DeepONet is represented by a summation of products of neural networks (NNs), corresponding to the branch NN for the input function and the trunk NN for the output function; both NNs are general architectures, e.g., the branch NN can be replaced with a CNN or a ResNet. According to Kovachki et al. (2021), FNO in its continuous form can be viewed conceptually as a DeepONet with a specific architecture of the branch NN and a trunk NN represented by a trigonometric basis. In order to compare FNO with DeepONet computationally for realistic setups, we develop several extensions of FNO that can deal with complex geometric domains as well as mappings where the input and output function spaces are of different dimensions. We also develop an extended DeepONet with special features that provide inductive bias and accelerate training, and we present a faster implementation of DeepONet with cost comparable to the computational cost of FNO, which is based on the Fast Fourier Transform. Here we consider 16 different benchmarks to demonstrate the relative performance of the two neural operators, including instability wave analysis in hypersonic boundary layers, prediction of the vorticity field of a flapping airfoil, porous media simulations in complex-geometry domains, etc. We follow the guiding principles of FAIR (Findability, Accessibility, Interoperability, and Reusability) for scientific data management and stewardship. The performance of DeepONet and FNO is comparable for relatively simple settings, but for complex geometries the performance of FNO deteriorates greatly. We also compare theoretically the two neural operators and obtain similar error estimates for DeepONet and FNO under the same regularity assumptions.

42 ENGINEERING↗
DOE OSTI2022

Approximation rates of DeepONets for learning operators arising from advection–diffusion equations

Here we present the analysis of approximation rates of operator learning in Chen and Chen (1995) and Lu et al. (2021), where continuous operators are approximated by a sum of products of branch and trunk networks. In this work, we consider the rates of learning solution operators from both linear and nonlinear advection–diffusion equations with or without reaction. We find that the approximation rates depend on the architecture of branch networks as well as the smoothness of inputs and outputs of solution operators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

($\mathrm{INVITED}$)Counter-ion effect on the diffusion behavior of $\mathrm{Y}$b, $\mathrm{L}$u, and $\mathrm{N}$d ions in $\mathrm{YAG}$ transparent ceramics

The ability to fabricate additively manufactured laser waveguides with sharp dopant concentration interfaces is limited by diffusion of the dopants at the temperatures required to fully densify the material. Compositional analysis of bilayer samples, where each layer was either undoped YAG or YAG doped with Yb, Lu, or Nd, were fabricated such that all combinations were available for testing. Samples were fabricated at both 1750°C and 1850°C to determine the diffusion behavior of each dopant alone and also in the presence of a second dopant. It was found that the experimental concentration profiles exhibited both intragranular (bulk) and grain boundary contributions, and thus fitting to a complementary error function equation required the use of two diffusion coefficients. Nd always diffused further along grain boundaries than the other dopants, due in part to its small segregation coefficient in YAG. It is shown that the presence of Nd as a counter dopant inhibits intragranular diffusion of other dopants while enhancing their grain boundary diffusion. All the observed trends were attributed to a combination of intragranular lattice strain due to: dopant ions replacing yttrium substitutionally, the relative driving forces for the segregation of dopant ions to grain boundaries, and the ability of one dopant to affect the diffusion of a different dopant in the other direction to maintain charge neutrality.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Coordination Sphere of Lanthanide Aqua Ions Resolved with Ab Initio Molecular Dynamics and X-ray Absorption Spectroscopy

To resolve the fleeting structures of lanthanide Ln 3+ aqua ions in solution, we (i) performed the first ab initio molecular dynamics (AIMD) simulations of the entire series of Ln 3+ aqua ions in explicit water solvent using pseudopotentials and basis sets recently optimized for lanthanides and (ii) measured the symmetry of the hydrating waters about Ln 3+ ions (Nd 3+ , Dy 3+ , Er 3+ , Lu 3+ ) for the first time with extended X-ray absorption fine structure (EXAFS). EXAFS spectra were measured experimentally and generated from AIMD trajectories to directly compare simulation, which concurrently considers the electronic structure and the atomic dynamics in solution, with experiment. In this study, we performed a comprehensive evaluation of EXAFS multiple-scattering analysis (up to 6.5 Å) to measure Ln–O distances and angular correlations (i.e., symmetry) and elucidate the molecular geometry of the first hydration shell. This evaluation, in combination with symmetry-dependent L 3 - and L 1 -edge spectral analysis, shows that the AIMD simulations remarkably reproduces the experimental EXAFS data. The error in the predicted Ln–O distances is less than 0.07 Å for the later lanthanides, while we observed excellent agreement with predicted distances within experimental uncertainty for the early lanthanides. Our analysis revealed a dynamic, symmetrically disordered first coordination shell, which does not conform to a single molecular geometry for most lanthanides. This work sheds critical light on the highly elusive coordination geometry of the Ln 3+ aqua ions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Mixed-precision iterative refinement using tensor cores on GPUs to accelerate solution of linear systems

Double-precision floating-point arithmetic (FP64) has been the de facto standard for engineering and scientific simulations for several decades. Problem complexity and the sheer volume of data coming from various instruments and sensors motivate researchers to mix and match various approaches to optimize compute resources, including different levels of floating-point precision. In recent years, machine learning has motivated hardware support for half-precision floating-point arithmetic. A primary challenge in high-performance computing is to leverage reduced-precision and mixed-precision hardware. We show how the FP16/FP32 Tensor Cores on NVIDIA GPUs can be exploited to accelerate the solution of linear systems of equations Ax = b without sacrificing numerical stability. The techniques we employ include multiprecision LU factorization, the preconditioned generalized minimal residual algorithm (GMRES), and scaling and auto-adaptive rounding to avoid overflow. We also show how to efficiently handle systems with multiple right-hand sides. On the NVIDIA Quadro GV100 (Volta) GPU, we achieve a 4×-5× performance increase and 5× better energy efficiency versus the standard FP64 implementation while maintaining an FP64 level of numerical stability.

GMRES↗
DOE OSTI2023

Optimizing Communication in 2D Grid-Based MPI Applications at Exascale

The new reality of exascale computing faces many challenges in achieving optimal performance on large numbers of nodes. A key challenge is the efficient utilization of the message-passing interface (MPI), a critical component for process communication. This paper explores communication optimization strategies to harness the GPU-accelerated architectures of these supercomputers. We focus on MPI applications where processors form a two-dimensional process grid, a common arrangement in applications involving dense matrix operations. This configuration offers a unique opportunity to implement innovative strategies to improve performance and maintain effective load distribution. We study two applications— Dist-FW (Apsp:all-pair-shortest-path) and HPL-MxP (LU factorization with Mixed precision)—on two accelerated systems: Summit (IBM Power and NVIDIA V100) and Frontier (AMD EPYC and MI250X). These supercomputers are operated by the Oak Ridge Leadership Computing Facility (OLCF) and are currently ranked #1 and #5 on the Top500 list. We show how to scale up both applications to exascale levels and tackle the MPI challenges related to implementation, synchronization, and performance. We also compare the performance of several communication strategies at an unprecedented scale. Accurately predicting application performance becomes crucial for cost reduction as the computational scale grows. To address this, we suggest a hyperbolic model as a better alternative to the traditional one-sided asymptotic model for predicting future application performance at such large scales.

Lu, Hao↗
DOE OSTI2020

Materials Data on LuH24C4N4Cl7 by Materials Project

(LuCl6)2(CH3NH3)8Cl2 is Silicon tetrafluoride-derived structured and crystallizes in the monoclinic P2 space group. The structure is zero-dimensional and consists of two hydrochloric acid molecules, eight methylammonium molecules, and two LuCl6 clusters. In each LuCl6 cluster, Lu3+ is bonded in an octahedral geometry to six Cl1- atoms. There are a spread of Lu–Cl bond distances ranging from 2.56–2.65 Å. There are three inequivalent Cl1- sites. In the first Cl1- site, Cl1- is bonded in a single-bond geometry to one Lu3+ atom. In the second Cl1- site, Cl1- is bonded in a single-bond geometry to one Lu3+ atom. In the third Cl1- site, Cl1- is bonded in a single-bond geometry to one Lu3+ atom.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Materials Data on Lu2MgSe4 by Materials Project

MgLu2Se4 is Spinel structured and crystallizes in the cubic Fd-3m space group. The structure is three-dimensional. Mg2+ is bonded to four equivalent Se2- atoms to form MgSe4 tetrahedra that share corners with twelve equivalent LuSe6 octahedra. The corner-sharing octahedral tilt angles are 57°. All Mg–Se bond lengths are 2.61 Å. Lu3+ is bonded to six equivalent Se2- atoms to form LuSe6 octahedra that share corners with six equivalent MgSe4 tetrahedra and edges with six equivalent LuSe6 octahedra. All Lu–Se bond lengths are 2.82 Å. Se2- is bonded in a distorted rectangular see-saw-like geometry to one Mg2+ and three equivalent Lu3+ atoms.

36 MATERIALS SCIENCE↗