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All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Compiler-Directed Transformation for Higher-Order Stencils

As the cost of data movement increasingly dominates performance, developers of finite-volume and finite-difference solutions for partial differential equations (PDEs) are exploring novel higher-order stencils that increase numerical accuracy and computational intensity. This paper describes a new compiler reordering transformation applied to stencil operators that performs partial sums in buffers, and reuses the partial sums in computing multiple results. This optimization has multiple effect son improving stencil performance that are particularly important to higher-order stencils: exploits data reuse, reduces floating-point operations, and exposes efficient SIMD parallelism to backend compilers. We study the benefit of this optimization in the context of Geometric Multigrid (GMG), a widely used method to solvePDEs, using four different Jacobi smoothers built from 7-, 13-, 27-and 125-point stencils. We quantify performance, speedup, andnumerical accuracy, and use the Roofline model to qualify our results. Ultimately, we obtain over 4× speedup on the smoothers themselves and up to a 3× speedup on the multigrid solver. Finally, we demonstrate that high-order multigrid solvers have the potential of reducing total data movement and energy by several orders of magnitude.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

The Data Mine model for accessible partnerships in data science

Abstract The Data Mine at Purdue University is a pioneering experiential learning community for undergraduate and graduate students of any background to learn data science. The first data‐intensive experience embedded in a large learning community, The Data Mine had nearly 1300 students in academic year (AY) 2022–2023 and nearly 1700 students for AY 2023–2024. The Data Mine embodies data‐infused education, research, and collaboration. Students learn Python, R, SQL, and shell‐scripting, while working on weekly projects within a high‐performance computing (HPC) cluster. In the Corporate Partners cohort, students work on teams of 5–15 students, led by a paid student team leader. Each cohort follows an Agile approach, working on data‐intensive projects provided by industry partners and mentored by company employees. Students develop professional and data skills throughout the academic year, from August through April. Many students return in subsequent years to the program, increasing their tenure with a Corporate Partner. Student teams are inherently interdisciplinary; students from 133 different majors are involved in the program, ranging from new incoming students through PhD level students. These interdisciplinary teams of students bring new perspectives to challenging problems in which data science is a key part of the solution. The interdisciplinary teams foster an environment of synthesis with ideas and solutions. Students come together with different life experiences, different levels of technical skill, but also varying ways they navigate paths to solutions because of the variety of majors represented, resulting in a more creative and robust solution than a traditional data science program. This article is categorized under: Applications of Computational Statistics > Education in Computational Statistics

Betz, Margaret A.↗
DOE OSTI2020

Rucio beyond ATLAS: experiences from Belle II, CMS, DUNE, EISCAT3D, LIGO/VIRGO, SKA, XENON

For many scientific projects, data management is an increasingly complicated challenge. The number of data-intensive instruments generating unprecedented volumes of data is growing and their accompanying workflows are becoming more complex. Their storage and computing resources are heterogeneous and are distributed at numerous geographical locations belonging to different administrative domains and organisations. These locations do not necessarily coincide with the places where data is produced nor where data is stored, analysed by researchers, or archived for safe long-term storage. To fulfil these needs, the data management system Rucio has been developed to allow the high-energy physics experiment ATLAS at LHC to manage its large volumes of data in an efficient and scalable way. But ATLAS is not alone, and several diverse scientific projects have started evaluating, adopting, and adapting the Rucio system for their own needs. As the Rucio community has grown, many improvements have been introduced, customisations have been added, and many bugs have been fixed. Additionally, new dataflows have been investigated and operational experiences have been documented. In this article we collect and compare the common successes, pitfalls, and oddities that arose in the evaluation efforts of multiple diverse experiments, and compare them with the ATLAS experience. This includes the high-energy physics experiments Belle II and CMS, the neutrino experiment DUNE, the scattering radar experiment EISCAT3D, the gravitational wave observatories LIGO and VIRGO, the SKA radio telescope, and the dark matter search experiment XENON.

Lassnig, Mario↗
DOE OSTI2014

Surface plasmon coupled emission studies on engineered thin film hybrids of nano α−Al{sub 2}O{sub 3} on silver

We report the first time engineering and fabrication of a novel thin film hybrid of nano α-alumina doped in a polyvinyl alcohol (PVA) matrix along with rhodamine b (Rh.B) on a silver thin film. Silver films of 50 nm thickness on glass slides were fabricated by thermal evaporation. Nano α-alumina was synthesized through the combustion route and characterized by XRD. The α-alumina was dispersed in the PVA-Rh.B matrix by tip sonication. The resultant solution was spin coated on the Ag thin film at 3000 rpm to generate an overcoat of ∼30 nm. We have designed and constructed an opto-mechanical setup for performing the SPCE studies. Excitation with a 532 nm continuous laser, led to the coupling of the energy of Rh.B emission to the surface plasmon modes of silver. The emission @ 580 nm was recorded using an Ocean Optics(copyright, serif) fiber optic spectrometer. Calculation of the ratio of signal intensity between the directional SPCE and isotropic fluorescence gives us the factor of signal enhancements which SPCE offers. We report an '8 fold' signal enhancement attributed to SPCE arising from the metal oxide doped thin film hybrid. We observed only a '5 fold' signal enhancement in the case of a thin film hybrid without α-alumina. The emission was also 92% P-polarized which is in coherence with the theory of SPCE. The greater degree of signal enhancement observed in the α-alumina doped thin film substrate can be attributed to the surface roughness which alumina offers to silver, which along with the porous nature of alumina enables a greater degree of adsorption of Rh.B which results in a higher emission intensity. Computational modeling was also performed, based on surface plasmon resonance (SPR) calculations to provide theoretical background to observed experimental data. The α-alumina thin film hybrid can be extended as an economical sensing platform towards the high sensitive detection of analytes.

36 MATERIALS SCIENCE↗
DOE OSTI2014

The 'last mile' of data handling: Fermilab's IFDH tools

IFDH (Intensity Frontier Data Handling), is a suite of tools for data movement tasks for Fermilab experiments and is an important part of the FIFE[2] (Fabric for Intensity Frontier [1] Experiments) initiative described at this conference. IFDH encompasses moving input data from caches or storage elements to compute nodes (the 'last mile' of data movement) and moving output data potentially to those caches as part of the journey back to the user. IFDH also involves throttling and locking to ensure that large numbers of jobs do not cause data movement bottlenecks. IFDH is realized as an easy to use layer that users call in their job scripts (e.g. 'ifdh cp'), hiding the low level data movement tools. One advantage of this layer is that the underlying low level tools can be selected or changed without the need for the user to alter their scripts. Logging and performance monitoring can also be added easily. This system will be presented in detail as well as its impact on the ease of data handling at Fermilab experiments.

Lyon, Adam L.↗
DOE OSTI2018

Electron-Hole Theory of the Effect of Quantum Nuclei on the X-Ray Absorption Spectra of Liquid Water

Electron-hole excitation theory is used to unveil the role of nuclear quantum effects on the x-ray absorption spectral signatures of water, whose structure is computed via path-integral molecular dynamics with the MB-pol intermolecular potential model. Compared to spectra generated from the classically modeled water, quantum nuclei introduce important effects on the spectra in terms of both the energies and line shapes. Fluctuations due to delocalized protons influence the short-range ordering of the hydrogen bond network via changes in the intramolecular covalence, which broaden the preedge spectra. For intermediate-range and long-range ordering, quantum nuclei approach the neighboring oxygen atoms more closely than classical protons, promoting an “icelike” spectral feature with the intensities shifted from the main edge to the postedge. Computed spectra are in nearly quantitative agreement with the available experimental data.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

GreenThrift: Optimizing Carbon and Cost for Flexible Residential Loads

Reducing buildings’ carbon emissions is an important sustainability challenge. While scheduling flexible building loads has been previously used for a variety of grid and energy optimizations, carbon footprint reduction using such flexible loads poses new challenges since such methods need to balance both energy and carbon costs while also reducing user inconvenience from delaying such loads. This paper highlights the potential conflict between electricity prices and carbon emissions and the resulting trade-offs in carbon-aware and cost-aware load scheduling. To address this trade-off, we propose GreenThrift, a home automation system that leverages the scheduling capabilities of smart appliances and knowledge of future carbon intensity and cost to reduce both the carbon emissions and costs of flexible energy loads. At the heart of GreenThrift is an optimization technique that automatically computes schedules based on user configurations and preferences. We evaluate the effectiveness of GreenThrift using real-world carbon intensity data, electricity prices, and load traces from multiple locations and across different scenarios and objectives. Our results show that GreenThrift can replicate the offline optimal and retains 97% of the savings when optimizing the carbon emissions. Moreover, we show how GreenThrift can balance the conflict between carbon and cost and retain 95.3% and 85.5% of the potential carbon and cost savings, respectively.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

A Software Defined Storage Approach to Exascale Storage Services (Final Technical Report)

The Mochi project began in 2015 in recognition of a set of changes that were set to alter the landscape of storage and I/O for DOE science. On the one hand, an influx of new classes of applications including an initial set of data intensive and machine learning applications were operating with new types of data and in new ways, as compared to traditional computational science simulation codes. Simultaneously, new memory, storage, and networking technologies were driving a need to adapt software designs to a rapidly changing design space. The goal of the three-year Mochi project was to develop and demonstrate an approach for composing storage services which provides new levels of functionality, performance, and reliability for science applications at extreme scale. The team, including a mix of Laboratory, university, and industry collaborators, focused on systems and applications of interest to DOE Office of Science, tracking developments at NERSC and the LCFs as well as application trends within ECP and SciDAC. Technical work focused on three themes: composition (understand the building blocks needed to provide storage services that meet DOE mission needs and how these components are flexibly composed), application interfacing (what service compositions are most effective for common DOE application use cases), and consistency and synchronization (taking advantage of knowledge about, and from, the system and scientific applications to provide better service than would otherwise be possible). The project was highly successful, producing 13 publications and tens of thousands of lines of open source software that is now being used by multiple ECP teams, HPC software vendors, and international collaborators. The uptake of Mochi technologies speaks to both the value of the service composition model and the quality of architecture and software developed by the team. Through continued funding under ASCR and ECP, the Mochi team and its technologies are on track to further contribute to DOE science successes into the exascale timeframe.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

The Science DMZ: A Network Design Pattern for Data-Intensive Science

The ever-increasing scale of scientific data has become a significant challenge for researchers that rely on networks to interact with remote computing systems and transfer results to collaborators worldwide. Despite the availability of high-capacity connections, scientists struggle with inadequate cyberinfrastructure that cripples data transfer performance, and impedes scientific progress. The Science DMZ paradigm comprises a proven set of network design patterns that collectively address these problems for scientists. We explain the Science DMZ model, including network architecture, system configuration, cybersecurity, and performance tools, that creates an optimized network environment for science. We describe use cases from universities, supercomputing centers and research laboratories, highlighting the effectiveness of the Science DMZ model in diverse operational settings. In all, the Science DMZ model is a solid platform that supports any science workflow, and flexibly accommodates emerging network technologies. As a result, the Science DMZ vastly improves collaboration, accelerating scientific discovery.

Dart, Eli↗
DOE OSTI2020

Data Driven Online Learning of Power System Dynamics

With the advancement of sensing and communication in power networks, high-frequency real-time data from a power network can be used as a resource to develop better monitoring capabilities. In this work, a systematic approach based on data-driven operator theoretic methods involving Koopman operator is proposed for the online identification of power system dynamics. In particular, a new algorithm is provided, which unlike any previously existing algorithms, updates the Koopman operator iteratively as new data points are acquired. The proposed algorithm has three advantages: a) allows for real-time monitoring of the power system dynamics b) linear power system dynamics (this linear system is usually in a higher dimensional feature space and is not same as linearization of the underlying nonlinear dynamics) and c) computationally fast and less intensive when compared to the popular Extended Dynamic Mode Decomposition (EDMD) algorithm. The efficiency of the proposed algorithm is illustrated on an IEEE 9 bus system using synthetic data from the nonlinear model and on IEEE 39 bus system using synthetic data from the linearized model.

Sinha, Subhrajit↗
DOE OSTI2026

Uncertain quantum computing futures and potential energy and physical resource impacts at scale

Considerable attention has recently focused on the vast energy and water demands of supercomputing, namely large-scale data centers that underpin artificial intelligence (AI), one of the great disruptors of contemporary society. Looking ahead some years from now, quantum computing is poised to disrupt established computing paradigms once again. Scientists and engineers are now working intensely to bring this century-old dream of physicists to fruition. Yet, as quantum computers begin to be integrated with classical supercomputing architectures, the implications for energy and physical resource use also need to be understood, especially how they compare to today’s AI data centers. These impacts have not yet been quantified by the research community – a notable gap in the literature, even if commercial-scale deployment of Quantum-Accelerated Computing Infrastructure (QuACI) is not expected for a few more years. This study is the first to conduct such an assessment. Using publicly available information from academic sources and private industry, we characterize multiple configurations of superconducting qubit-based, fault-tolerant quantum computers (FTQC) that could plausibly be deployed at scale in the 2030s and into the 2040s. By parameterizing these FTQC systems at a process level, we conduct a prospective scenario analysis to quantify their energy and physical resource needs. While these estimates are uncertain, given the current state of quantum technologies and their unknown future trajectories, important insights can already be drawn. One key finding is that while the electricity needs for a fleet of FTQCs are within the bounds of previous modeling studies that have explored high electricity demand futures, the needs for certain physical resources, namely water and helium-3, could pose bottlenecks to QuACI scale-up.

Computing↗
DOE OSTI2014

Big-Data RHEED analysis for understanding epitaxial film growth processes

Reflection high energy electron diffraction (RHEED) has by now become a standard tool for in-situ monitoring of film growth by pulsed laser deposition and molecular beam epitaxy. Yet despite the widespread adoption and wealth of information in RHEED image, most applications are limited to observing intensity oscillations of the specular spot, and much additional information on growth is discarded. With ease of data acquisition and increased computation speeds, statistical methods to rapidly mine the dataset are now feasible. Here, we develop such an approach to the analysis of the fundamental growth processes through multivariate statistical analysis of RHEED image sequence. This approach is illustrated for growth of LaxCa1-xMnO3 films grown on etched (001) SrTiO3 substrates, but is universal. The multivariate methods including principal component analysis and k-means clustering provide insight into the relevant behaviors, the timing and nature of a disordered to ordered growth change, and highlight statistically significant patterns. Fourier analysis yields the harmonic components of the signal and allows separation of the relevant components and baselines, isolating the assymetric nature of the step density function and the transmission spots from the imperfect layer-by-layer (LBL) growth. These studies show the promise of big data approaches to obtaining more insight into film properties during and after epitaxial film growth. Furthermore, these studies open the pathway to use forward prediction methods to potentially allow significantly more control over growth process and hence final film quality.

Big-Data↗
DOE OSTI2026

Tachyon: Intelligent Multi-Scale Modeling of Distributed Resilient Infrastructure and Workflows for Data Intensive HEP Analyses

The DOE High Energy Physics (HEP) program in Neutrino and Collider science drives data-intensive science and simulation on extreme-scale platforms. Modeling and optimizing the complex distributed components from experimental to leadership computing facilities are essential for HEP workflows to achieve required response times and resilience under various conditions. Tachyon proposes a framework for scalable modeling, simulation, and validation of key performance characteristics for the distributed infrastructure between FNAL and ALCF, along with associated HEP workflows.

Carothers, Chris [Rensselaer Poly.]↗
DOE OSTI2026

Tachyon: Intelligent Multi-Scale Modeling of Distributed Resilient Infrastructure and Workflows for Data Intensive HEP Analyses

The DOE High Energy Physics (HEP) program in Neutrino and Collider science drives data-intensive science and simulation on extreme-scale platforms. Modeling and optimizing the complex distributed components from experimental to leadership computing facilities are essential for HEP workflows to achieve required response times and resilience under various conditions. Tachyon proposes a framework for scalable modeling, simulation, and validation of key performance characteristics for the distributed infrastructure between FNAL and ALCF, along with associated HEP workflows.

Carothers, Chris [Rensselaer Poly.]↗
DOE OSTI2025

Quantum Time Dynamics Mediated by the Yang–Baxter Equation and Artificial Neural Networks

Quantum computing shows great potential, but errors pose a significant challenge. This study explores new strategies for mitigating quantum errors using artificial neural networks (ANNs) and the Yang–Baxter equation (YBE). Unlike traditional error mitigation methods, which are computationally intensive, we investigate artificial error mitigation. We developed a novel method that combines ANNs for noise mitigation combined with the YBE to generate noisy data. This approach effectively reduces noise in quantum simulations, enhancing the accuracy of the results. The YBE rigorously preserves quantum correlations and symmetries in spin chain simulations in certain classes of integrable lattice models, enabling effective compression of quantum circuits while retaining linear scalability with the number of qubits. This compression facilitates both full and partial implementations, allowing the generation of noisy quantum data on hardware alongside noiseless simulations using classical platforms. By introducing controlled noise through the YBE, we enhance the data set for error mitigation. We train an ANN model on partial data from quantum simulations, demonstrating its effectiveness in mitigating errors in time-evolving quantum states, providing a scalable framework to enhance quantum computation fidelity, particularly in noisy intermediate-scale quantum (NISQ) systems. We demonstrate the efficacy of this approach by performing quantum time dynamics simulations using the Heisenberg XY Hamiltonian on real quantum devices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

SURROGATE MODEL DEVELOPMENT AND VALIDATION FOR RELIABILITY ANALYSIS OF REACTOR PRESSURE VESSELS

In nuclear light water reactors (LWRs), the reactor coolant, core and shroud are contained within a massive, thick walled steel vessel known as a reactor pressure vessel (RPV). Given the tremendous size of these structures, RPVs typically contain a large population of pre-existing flaws introduced in the manufacturing process. After many years of operation, irradiation-induced embrittlement makes these vessels increasingly susceptible to fracture initiation at the locations of the pre-existing flaws. Because of the uncertainty in the loading conditions, flaw characteristics and material properties, probabilistic methods are widely accepted and used in assessing RPV integrity. The Fracture Analysis of Vessels – Oak Ridge (FAVOR) computer program developed by researchers at Oak Ridge National Laboratory is widely used for this purpose. This program can be used in order to perform deterministic and probabilistic risk-informed analyses of the structural integrity of an RPV subjected to a range of thermal-hydraulic events. FAVOR uses a one-dimensional representation of the global response of the RPV, which is appropriate for the beltline region, which experiences the most embrittlement, and employs an influence coefficient technique to rapidly compute stress intensity factors for axis-aligned surface-breaking flaws. The Grizzly code is currently under development at Idaho National Laboratory (INL) to be used as a general multiphysics simulation tool to study a variety of degradation mechanisms in nuclear power plant components. The first application of Grizzly has been to study fracture in embrittled RPVs. Grizzly can be used to model the thermo-mechanical response of an RPV under transient conditions observed in a pressurized thermal shock (PTS) scenario. The global response of the vessel provides boundary conditions for local 3D models of the material in the vicinity of a flaw. Fracture domain integrals are computed to obtain stress intensity factors, which can in turn be used to assess whether a fracture would initiate at a pre-existing flaw. To use Grizzly for probabilistic analysis, it is necessary to have a way to rapidly evaluate stress intensity factors. To accomplish this goal, a reduced order model (ROM) has been developed to efficiently represent the behavior of a detailed 3D Grizzly model used to calculate fracture parameters. This approach uses the stress intensity factor influence coefficient method that has been used with great success in FAVOR. Instead of interpolating between tabulated solutions, as FAVOR does, the ROM approach uses a response surface methodology to compute fracture solutions based on a sampled set of results used to train the ROM. The main advantages of this approach are that the process of generating the training data can be fully automated, and the procedure can be readily used to consider more general flaw configurations. This paper demonstrates the procedure used to generate a ROM to rapidly compute stress intensity factors for axis-aligned flaws. The results from this procedure are in good agreement with those produced using the traditional influence coefficient interpolation procedure, which gives confidence in this method. This paves the way for applying this procedure for more general flaw configurations.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

The Improved Simple Corner Balance Method and Efforts to Enhance its Computational Performance

The diamond difference (DD) spatial discretization has long been a work horse of the neutron transport community. For nearly as long as DD has been used in neutron transport, it has been observed that DD produces undamped, non-physical angular fluxes in spatially under resolved problems and can fail in the thick diffusion limit. A long list of alternative spatial discretizations, all with better physics properties: accuracy, robustness, and/or preservation of the thick diffusion limit, have been proposed as alternatives to DD. Unfortunately, these alternative spatial discretizations also require significantly more floating point operations (FLOPs) per unknown compared to DD. Historically, neutron transport problems of interest have been able to be resolved sufficiently that DD is accurate enough and FLOPs have been the computational performance bottleneck controlling time to solution. Thus, in practice DD has been and remains a preferred spatial discretization for neutron transport simulations. Trends in computing architecture are leading to a new paradigm where data movement, not FLOPs will control time to solution. This presents an opportunity to improve neutron transport physics by reconsidering more FLOP intensive spatial discretization than DD without incurring a time to solution penalty. In this work, we consider the improved simple corner balance (ISCB) spatial discretization, a unique alternative to DD that has the same memory footprint as DD, and detail efforts to improve its computational performance. The ISCB spatial discretization is a more FLOP intensive spatial discretization than DD with the same memory footprint as DD. Though designed to maximize FLOP intensity, third party libraries such as LAPACK are not tuned for the unique, frequent, small linear algebra needs of neutron transport problems. While hand coding a sparse Gauss elimination is likely to be faster than a dense matrix solve (CM vs. HGE), were LAPACK suitable for small linear algebra problems, we would not have seen a performance increase with decreased numbers of LAPACK calls for the Schur complement solve (SC4 vs. SC1). Significant effort has reduced the relative compute time of ISCB to DD, but on current computer architectures DD is still faster than ISCB. Future work comparing the overall efficiency of ISCB to DD may reveal that while ISCB is slower than DD on current computers, ISCB is more accurate than DD by a factor greater than the compute time slowdown. As new computer architectures become available, we will test our hypothesis that neutron transport simulations will be bandwidth limited, ideally rendering the additional FLOPs required by ISCB to be 'free' [in terms of time to solution]. (authors)

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

MICCO: An Enhanced Multi-GPU Scheduling Framework for Many-Body Correlation Functions

Calculation of many-body correlation functions is one of the critical kernels utilized in many scientific computing areas, especially in Lattice Quantum Chromodynamics (Lattice QCD). It is formalized as a sum of a large number of contraction terms each of which can be represented by a graph consisting of vertices describing quarks inside a hadron node and edges designating quark propagations at specific time intervals. Due to its computation- and memory-intensive nature, real-world physics systems (e.g., multi-meson or multi-baryon systems) explored by Lattice QCD prefer to leverage multi-GPUs. Different from general graph processing, many-body correlation function calculations show two specific features: a large number of computation-/data-intensive kernels and frequently repeated appearances of original and intermediate data. The former results in expensive memory operations such as tensor movements and evictions. The latter offers data reuse opportunities to mitigate the data-intensive nature of many-body correlation function calculations. However, existing graph-based multi-GPU schedulers cannot capture these data-centric features, thus resulting in a sub-optimal performance for many-body correlation function calculations. To address this issue, this paper presents a multi-GPU scheduling framework, MICCO, to accelerate contractions for correlation functions particularly by taking the data dimension (e.g., data reuse and data eviction) into account. This work first performs a comprehensive study on the interplay of data reuse and load balance, and designs two new concepts: local reuse pattern and reuse bound to study the opportunity of achieving the optimal trade-off between them. Based on this study, MICCO proposes a heuristic scheduling algorithm and a machine-learning-based regression model to generate the optimal setting of reuse bounds. Specifically, MICCO is integrated into a real-world Lattice QCD system, Redstar, for the first time running on multiple GPUs. The evaluation demonstrates MICCO outperforms other state-of-art works, achieving up to 2.25× speedup in synthesized datasets, and 1.49× speedup in real-world correlation functions.

Wang, Qihan↗