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DOE OSTI2022

MAPredict: Static Analysis Driven Memory Access Prediction Framework for Modern CPUs

Application memory access patterns are crucial in deciding how much traffic is served by the cache and forwarded to the dynamic random-access memory (DRAM). However, predicting such memory traffic is difficult because of the interplay of prefetchers, compilers, parallel execution, and innovations in manufacturer-specific micro-architectures. This research introduced MAPredict, a static analysis-driven framework that addresses these challenges to predict last-level cache (LLC)-DRAM traffic. By exploring and analyzing the behavior of modern Intel processors, MAPredict formulates cache-aware analytical models. MAPredict invokes these models to predict LLC-DRAM traffic by combining the application model, machine model, and user-provided hints to capture dynamic information. MAPredict successfully predicts LLC-DRAM traffic for different regular access patterns and provides the means to combine static and empirical observations for irregular access patterns. Evaluating 130 workloads from six applications on recent Intel micro-architectures, MAPredict yielded an average accuracy of 99% for streaming, 91% for strided, and 92% for stencil patterns. By coupling static and empirical methods, up to 97% average accuracy was obtained for random access patterns on different micro-architectures.

Monil, M. A. H.↗

DOE OSTI2022

Developing an ELM Ecosystem Dynamics Model on GPU with OpenACC

Porting a complex scientific code, such as the E3SM land model (ELM), onto a new computing architecture is challenging. The paper presents design strategies and technical approaches to develop an ELM ecosystem dynamics model with compiler directives (OpenACC) on NVIDIA GPUs. The code has been refactored with advanced OpenACC features (such as deepcopy and routine directives) to reduce memory consumption and to increase the levels of parallelism through parallel loop reconstruction and new data structures. As a result, the optimized parallel implementation achieved more than a 140-time speedup (50 ms vs 7600 ms), compared to a naive implementation that uses OpenACC routine directive and parallelizes the code across existing loops on a single NVIDIA V100. On a fully loaded computing node with 44 CPUs and 6 GPUs, the code achieved over a 3.0-times speedup, compared to the original code on the CPU. Furthermore, the memory footprint of the optimized parallel implementation is 300 MB, which is around 15% of the 2.15 GB of memory consumed by a naive implementation. This study is the first effort to develop the ELM component on GPUs efficiently to support ultra-high-resolution land simulations at continental scales.

Schwartz, Peter↗

DOE OSTI2023

Developing Ultrahigh-Resolution E3SM Land Model for GPU Systems

Designing and refactoring complex scientific code, such as the E3SM land model (ELM), for new computing architectures is challenging. This paper presents design strategies and technical approaches to develop a data-oriented, GPU-ready ELM model using compiler directives (OpenACC/OpenMP). We first analyze the datatypes and processes in the original ELM code. Then we present design considerations for ultrahigh-resolution ELM (uELM) development for massive GPU systems. These techniques include the global data-oriented simulation workflow, domain partition, code porting and data copy, memory reduction, parallel loop restructure and flattening, and race condition detection. We implemented the first version of uELM using OpenACC targeting the NVidia GPUs in the Summit supercomputer at Oak Ridge National Laboratory. During the implementation, we developed a software tool (named SPEL) to facilitate code generation, verification, and performance tuning using these techniques. The first uELM implementation for Nvidia GPUs on Summit delivered promising results: 1) over 98% of the ELM code was automatically generated and tuned by scripts. Most ELM modules had better computational performances than the original ELM code for CPUs. The GPU-ready uELM is more scalable than the CPU code on fully-loaded Summit nodes. Example profiling results from several modules are also presented to illustrate the performance improvements and race condition detection. The lessons learned and toolkit developed in the study are also suitable for further uELM deployment using OpenMP on the first US exascale computer, Frontier, equipped with AMD CPUs and GPUs.

Schwartz, Peter↗

DOE OSTI2024

Quantifying the Potential of Argon Detection Capabilities for Nuclear Explosion Monitoring

Abstract Current noble gas detection systems for nuclear explosion monitoring are based on the detection of four radioxenon isotopes—Xe-131m, -133, -133m and -135. The data provided by radioxenon detection could be enhanced by other radionuclide signatures such as Ar-37. Activation of Ca-40 in rock by neutrons produces Ar-37, and monitoring for this additional nuclide could help distinguish detections of nuclear explosions from background sources, such as medical isotope production. This work studies the capabilities of a hypothetical argon detection network. A 10 kt explosion was modeled using MCNP and SCALE to determine the inventory of Ar-37 created in a representative granite rock layer, assuming either 0.1, 1 or 10% of the total inventory was released. The Ar-37 inventory was combined with atmospheric transport data from HYSPLIT compiled in a previous study, along with the detection limits of standard Ar-37 detection systems, to determine how many hypothetical monitoring stations would detect Ar-37 from an explosion. This method was repeated for 365 HYSPLIT data sets to create a year’s worth of hypothetical explosions, releases, and detections. The study quantified the average number of detections per release, the number of stations detecting Ar-37, and the possibility of detecting Ar-37 in coincidence with xenon.

37Ar↗

DOE OSTI2020

Yb:Lu 2 O 3 hydrothermally grown single-crystal high-resolution absorption spectra obtained between 8 and 300 K

Here we present new high-resolution absorption data for the important sesquioxide laser material Yb:Lu 2 O 3 for the spectral range of 880–1020 nm, at various temperatures between 8 and 300 K, and for the zero-phonon region from 960 to 990 nm, at temperatures from 8 to 300 K. We have experimentally observed the C 3i (0,1)–(1,3) transition for the first time, located at 880.7 nm at 8 K. Based on high confidence fitting functions to the experimental data, we provide the first complete compilation of all observed electronic and electronic–vibrational transitions. Detailed fitting and plots of the C 2 and C 3i zero-line data show an evolution of the linewidth from being predominantly electronic below about 100 K to being dominated by thermal processes above 100 K. We have also found evidence for a “soft” phase transition between 80 and 100 K that changes the local coordination environment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Perspectives on Few-Body Cluster Structures in Exotic Nuclei

It is a fascinating phenomenon in nuclear physics that states with a pronounced few-body structure can emerge from the complex dynamics of many nucleons. Such halo or cluster states often appear near the boundaries of nuclear stability. As such, they are an important part of the experimental program beginning at the Facility for Rare Isotope Beams (FRIB). A concerted effort of theory and experiment is necessary both to analyze experiments involving effective few-body states, as well as to constrain and refine theories of the nuclear force in light of new data from these experiments. Here, as a contribution to exactly this effort, this paper compiles a collection of “perspectives” that emerged out of the Topical Program “Few-body cluster structures in exotic nuclei and their role in FRIB experiments” that was held at FRIB in August 2022 and brought together theorists and experimentalists working on this topic.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Review: Pre-Darcy flows in low-permeability porous media

The widely used Darcy’s law specifies a linear relation between the Darcy velocity of fluid flow and the pressure gradient that drives the flow. However, studies have shown that Darcy velocity can exhibit a nonlinear dependence on the pressure gradient in low-permeability porous media such as clay and shale when the pressure gradient is adequately low. This phenomenon is referred to as low-velocity non-Darcian flow or pre-Darcy flow. This paper provides a comprehensive review of the theories, experimental data, and modeling methods for pre-Darcy flow in low-permeability porous media. The review begins by outlining the fundamental mechanisms underlying pre-Darcy flow that regulate the unique characteristics such as nonlinear dependence of the Darcy velocity on the pressure gradient and its relevance to fluid–rock interactions. The review then proceeds to present a thorough compilation of experimental investigations performed in various low-permeability geomaterials including tight sandstones, shales, and clays. Next, empirical and theoretical models and simulation methods that have been developed to fit and interpret experimental data are reviewed. Finally, the review underscores the challenges encountered in conducting and interpreting pre-Darcy flow experiments and suggests future research directions. By analyzing previous experimental investigations, this review aims to offer a valuable resource for researchers and practitioners seeking to enhance their understanding of fluid dynamics in low-permeability geomaterials. This provides insights into the application of pre-Darcy flow in numerous natural and engineered processes such as shale oil and gas recovery, contaminant transport in low-permeability aquifers, and geological disposal of nuclear waste.

58 GEOSCIENCES↗

DOE OSTI2019

Decadal carbon decomposition dynamics in three peatlands in Northern Minnesota

The uppermost portion of the peat profile, an area of active diagenetic processes, is exceedingly important for understanding peatland dynamics and the diagenesis and geochemistry of atmospherically-deposited materials. In this study, we investigated high resolution carbon (C) accrual and peat decomposition rates at two Sphagnum-rich ombrotrophic bogs and one fen in northern Minnesota, USA by analyzing 1 cm increments from 30 cm thick intact frozen blocks of peat soil. We conducted radiocarbon analysis of Sphagnum cellulose to determine peat age and net C accumulation at each depth interval. Calibrated peat ages were determined using CALIBomb and a compilation of calibration datasets for the pre-bomb period. We fit data with a negative exponential accumulation model and used model-derived parameters to estimate net primary productivity (NPP) and a peat decomposition rate constant k. FTIR spectroscopy and C:N were used to derive humification indices and to chemically characterize the peat. NPP ranged from 180 to 266 g C m -2 year -1 , k ranged from 0.015 to 0.019 year -1 . Net C accumulation rates ranged from 112 to 174 g C m -2 year -1 at 25 years and 70 to 113 g C m -2 year-1 at 50 years. Mass loss was up to 55% during the first 50 years of peat accumulation. Decomposition is greater at depth in the bogs—where 25 cm of peat correspond to 55 years of peat accumulation—than in the fen, where peat age is approximately 25 years at 25 cm depth. Lastly, information on fine-scale variations in peat mass decomposition and loss across ombrotrophic bogs and a fen help interpret other diagenetic processes in peatlands.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

How much carbon can be added to soil by sorption?

Abstract Quantifying the upper limit of stable soil carbon storage is essential for guiding policies to increase soil carbon storage. One pool of carbon considered particularly stable across climate zones and soil types is formed when dissolved organic carbon sorbs to minerals. We quantified, for the first time, the potential of mineral soils to sorb additional dissolved organic carbon (DOC) for six soil orders. We compiled 402 laboratory sorption experiments to estimate the additional DOC sorption potential, that is the potential of excess DOC sorption in addition to the existing background level already sorbed in each soil sample. We estimated this potential using gridded climate and soil geochemical variables within a machine learning model. We find that mid- and low-latitude soils and subsoils have a greater capacity to store DOC by sorption compared to high-latitude soils and topsoils. The global additional DOC sorption potential for six soil orders is estimated to be 107 $$\pm$$ ± 13 Pg C to 1 m depth. If this potential was realized, it would represent a 7% increase in the existing total carbon stock.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Molten Halide Salt Surface Tension: Methods and Correlations

Here, this paper reviews various methods for studying surface tension and their applicability to fluoride and chloride molten salt systems, including a comparison of benefits and drawbacks. Such a comparison aids in experiment design based on desired factors such as scale, accuracy, and repeatability. A detailed review is presented for existing literature data regarding the surface tension of molten fluoride and chloride salts. These reference data were compiled and analyzed to determine cross-validated correlation equations for several alkali and alkaline earth fluoride and chloride salts as functions of temperature. These correlations are necessary for reliable multiphysics modeling approaches as well as accurate design and analysis of multiphase molten salt phenomena such as gas sparging and bubble formation/transport. This analysis supports the development of the thermophysical arm of the Molten Salt Thermal Properties Database (MSTDB-TP) managed by Oak Ridge National Laboratory.

Chloride↗

DOE OSTI2021

Efficient Trainability of Linear Optical Modules in Quantum Optical Neural Networks

The existence of “barren plateau landscapes” for generic discrete-variable quantum neural networks, which obstructs efficient gradient-based optimization of cost functions defined by global measurements, would be surprising in the case of generic linear optical modules in quantum optical neural networks due to the tunability of the intensity of continuous variable states and the relevant unitary group having exponentially smaller dimension. We demonstrate that coherent light in m modes can be generically compiled efficiently if the total intensity scales sublinearly with m, and extend this result to cost functions based on homodyne, heterodyne, or photon detection measurement statistics, and to noisy cost functions in the presence of attenuation. Here, we further demonstrate efficient trainability of m mode linear optical quantum circuits for variational mean field energy estimation of positive quadratic Hamiltonians for input states that do not have energy exponentially vanishing with m.

47 OTHER INSTRUMENTATION↗

DOE OSTI2019

Recommended nuclear data for medical radioisotope production: diagnostic positron emitters

An IAEA coordinated research project that began in 2012 and ended in 2016 was primarily dedicated to the compilation, evaluation and recommendation of cross-section data for the production of medical radionuclides. One significant part of this work focused on diagnostic positron emitters. These particular studies consist of 69 reactions for direct and indirect or generator production of 44 Sc( 44 Ti), 52 mMn( 52 Fe), 52 gMn, 55 Co, 61 Cu, 62 Cu( 62 Zn), 66 Ga, 68 Ga( 68 Ge), 72 As( 72 Se), 73 Se, 76 Br, 82 Rb( 82 Sr), 82 mRb, 86 Y, 89 Zr, 90 Nb, 94 mTc, 110 mIn( 110 Sn), 118 Sb( 118 Te), 120 I, 122 I( 122 Xe), 128 Cs( 128 Ba), and 140 Pr( 140 Nd) medical radionuclides. The resulting reference cross-section data were obtained from Padé fits to selected and corrected experimental data, and integral thick target yields were subsequently deduced. Uncertainties in the fitted results were estimated via a Padé least-squares method with the addition of a 4% assessed systematic uncertainty. Experimental data were also compared with theoretical predictions available from the TENDL-2015 and TENDL-2017 libraries.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2019

Results from the third Galaxy Serpent tabletop exercise demonstrating the utility of nuclear forensics libraries in support of an investigation

Galaxy Serpent is a virtual, web-based international ongoing series of tabletop exercises designed to mature the concept of National Nuclear Forensics Libraries (NNFL) and illustrate their utility in answering investigative questions or providing investigative leads as part of an investigation involving nuclear or other radioactive material (R/N) out of regulatory control (MORC). Two prior versions of the exercise have been conducted; the first utilizing surrogate data for spent nuclear fuel as the material of interest, and the second using synthetic sealed radioactive source data. Teams participating in the recently concluded third version of the exercise were provided with synthetic uranium ore concentrate (UOC) data mimicking actual data characteristics (e.g. trace element concentrations), which was used to compile a model NNFL. Teams decided how to organize and interpret the data they were provided, dealt with real world features such as missing data, and assessed discriminating attributes. Next, a hypothetical scenario involving three UOC samples found out of regulatory control was provided, and each team had to assess the self-consistency of the individual sources in the unknown data, and employ their model NNFL as a comparative assessment instrument to determine whether the three UOC materials in question were consistent with any of the types of materials in their library. In conclusion, the design of the exercise, methodologies utilized by participating teams, and aggregate results of the exercise will be presented, along with challenges encountered and benefits realized.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2019

Efficiently embedding QUBO problems on adiabatic quantum computers

Adiabatic quantum computers like the D-Wave 2000Q can approximately solve the QUBO problem, which is an NP-hard problem, and have been shown to outperform classical computers on several instances. Solving the QUBO problem literally means solving virtually any NP-hard problem like the traveling salesman problem, airline scheduling problem, protein folding problem, genotype imputation problem, thereby enabling significant scientific progress, and potentially saving millions/billions of dollars in logistics, airlines, healthcare and many other industries. Yet, before QUBO problems are solved on quantum computers, they must be embedded (or compiled) onto the hardware of quantum computers, which in itself is a very hard problem. Here, we propose an efficient embedding algorithm, that lets us embed QUBO problems fast, uses less qubits and gets the objective function value close to the global minimum value. We then compare the performance of our embedding algorithm to that of D-Wave’s embedding algorithm, which is the current state of the art, and show that our embedding algorithm convincingly outperforms D-Wave’s embedding algorithm. Our embedding approach works with perfect Chimera graphs, i.e., Chimera graphs with no missing qubits.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

The Surface-Topography Challenge: A Multi-Laboratory Benchmark Study to Advance the Characterization of Topography

Surface performance is critically influenced by topography in virtually all real-world applications. The current standard practice is to describe topography using one of a few industry-standard parameters. The most commonly reported number is Ra, the average absolute deviation of the height from the mean line (at some, not necessarily known or specified, lateral length scale). However, other parameters, particularly those that are scale-dependent, influence surface and interfacial properties; for example the local surface slope is critical for visual appearance, friction, and wear. The present Surface-Topography Challenge was launched to raise awareness for the need of a multi-scale description, but also to assess the reliability of different metrology techniques. In the resulting international collaborative effort, 153 scientists and engineers from 64 research groups and companies across 20 countries characterized statistically equivalent samples from two different surfaces: a “rough” and a “smooth” surface. The results of the 2088 measurements constitute the most comprehensive surface description ever compiled. We find wide disagreement across measurements and techniques when the lateral scale of the measurement is ignored. Consensus is established through scale-dependent parameters while removing data that violates an established resolution criterion and deviates from the majority measurements at each length scale. Our findings suggest best practices for characterizing and specifying topography. The public release of the accumulated data and presented analyses enables global reuse for further scientific investigation and benchmarking.

42 ENGINEERING↗

DOE OSTI2019

A New Approach to Multivariate Network Traffic Analysis

© 2019, Springer Science+Business Media, LLC & Science Press, China. Network traffic analysis is one of the core functions in network monitoring for effective network operations and management. While online traffic analysis has been widely studied, it is still intensively challenging due to several reasons. One of the primary challenges is the heavy volume of traffic to analyze within a finite amount of time due to the increasing network bandwidth. Another important challenge for effective traffic analysis is to support multivariate functions of traffic variables to help administrators identify unexpected network events intuitively. To this end, we propose a new approach with the multivariate analysis that offers a high-level summary of the online network traffic. With this approach, the current state of the network will display patterns compiled from a set of traffic variables, and the detection problems in network monitoring (e.g., change detection and anomaly detection) can be reduced to a pattern identification and classification problem. In this paper, we introduce our preliminary work with clustered patterns for online, multivariate network traffic analysis with the challenges and limitations we observed. We then present a grid-based model that is designed to overcome the limitations of the clustered pattern-based technique. We will discuss the potential of the new model with respect to the technical challenges including streaming-based computation and robustness to outliers.

Kim, Jinoh↗

DOE OSTI2019

Determination of the Charge Materials Range in Multistage Charge Burden Optimization for Foundry Furnaces

Optimization of charge burden for foundry furnaces lies in determining a percentage or mass (kg, t) of the share of each charge material in the burden with the lowest unit cost. A classic task of charge burden optimization requires a definition of an objective function, which most often defines the cost of the charge burden and a system of constraints, including technological assumptions such as the balance of chemical elements for the required chemical composition of the charge or molten metal, limitations of the mass fraction of the individual elements of the charge, and others. The solution for such an optimization task is a set of starting values for the devices which weigh and proportion the charge materials for a foundry furnace. The consequences of deviations from proper values, causes by inaccurate weighing or proportioning and human error as well, can be limited by using an appropriately designed and implemented multistage optimization algorithm. This article presents a mathematical model of such an algorithm and discussion of its practical application. The algorithm as described below can be an important element of computer-aided decision to be made by employees in the process of batch compilation or automatically control devices dosing individual batch components. An IT system with an implemented algorithm may significantly increase the efficiency of charge preparation for a foundry furnace, guaranteeing its correct chemical composition despite batch materials dosing errors. It will contribute to a significant improvement of economic indicators of liquid metal production, with the effect of boosting the competitiveness of the foundry industry.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Potential of the Julia Programming Language for High Energy Physics Computing

Research in high energy physics (HEP) requires huge amounts of computing and storage, putting strong constraints on the code speed and resource usage. To meet these requirements, a compiled high-performance language is typically used; while for physicists, who focus on the application when developing the code, better research productivity pleads for a high-level programming language. A popular approach consists of combining Python, used for the high-level interface, and C++, used for the computing intensive part of the code. A more convenient and efficient approach would be to use a language that provides both high-level programming and high-performance. The Julia programming language, developed at MIT especially to allow the use of a single language in research activities, has followed this path. In this paper the applicability of using the Julia language for HEP research is explored, covering the different aspects that are important for HEP code development: runtime performance, handling of large projects, interface with legacy code, distributed computing, training, and ease of programming. The study shows that the HEP community would benefit from a large scale adoption of this programming language. The HEP-specific foundation libraries that would need to be consolidated are identified.

97 MATHEMATICS AND COMPUTING↗