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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

MOSIQS: Persistent Memory Object Storage With Metadata Indexing and Querying for Scientific Computing

Scientific applications often require high-bandwidth shared storage to perform joint simulations and collaborative data analytics. Shared memory pools provide a chance to satisfy such needs. Recently, a high-speed network such as Gen-Z utilizing persistent memory (PM) offers an opportunity to create a shared memory pool connected to compute nodes. However, there are several challenges to use scientific applications on the shared memory pool directly such as scalability, failure-atomicity, and lack of scientific metadata-based search and query. In this paper, we propose MOSIQS, a persistent memory object storage framework with metadata indexing and querying for scientific computing. We design MOSIQS based on the key idea that memory objects on PM pool can live beyond the application lifetime and can become the sharing currency for applications and scientists. MOSIQS provides an aggregate memory pool atop an array of persistent memory devices to store and access memory objects to accelerate scientific computing. MOSIQS uses a lightweight persistent memory key-value store to manage the metadata of memory objects, which enables memory object sharing. To facilitate metadata search and query over millions of memory objects resident on memory pool, we introduce Group Split and Merge (GSM), a novel persistent index data structure designed primarily for scientific datasets. GSM splits and merges dynamically to minimize the query search space and maintains low query processing time while overcoming the index storage overhead. MOSIQS is implemented on top of PMDK. We evaluate the proposed approach on many-core server with an array of real PM devices. Experimental results show that MOSIQS gains a 100% write performance improvement and executes multi-attribute queries efficiently with 2.7× less index storage overhead offering significant potential to speed up scientific computing applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Vibration-Based Techniques for Concrete Structural Health Monitoring. Light Water Reactor Sustainability Program report

The objectives of this ongoing research project focus on health monitoring and data analytics of concrete slabs containing reactive aggregates and thus subjected to degradation due to alkali-silica reaction (ASR). A controlled concrete slab with four pockets of reactive aggregates (pure silica, and reactive aggregates from three different quarries) was cast at Vanderbilt University and cured in representative conditions to accelerate degradation due to ASR. A set of four concrete samples were also cast and cured at the University of Alabama for ASR testing. Of these four samples, two slabs contained reactive aggregates while the other two had the non-reactive aggregate counterparts mixed throughout the samples. Four additional slabs were cast and cured at the University of Nebraska- Lincoln. Of these four samples; one contained non-reactive aggregates and no reinforcement, one contained reactive aggregates and no reinforcement, one contained reactive aggregates and rebar reinforcement in one direction, and the last contained reactive aggregates and reinforcement in two directions. Single frequency and dual frequency tests were used on these slabs to locate ASR damage within the specimens containing reactive aggregates. The vibration tests were conducted with different sensor and actuator configurations to facilitate damage detection and localization. The results from the data analysis of the dynamic testing on concrete slabs are discussed in detail in this report. Results for damage localization are dependent on multiple parameters used in the tests. This report investigates different methods of data fusion including voting, raw averaging, damage index averaging, and Bayesian fusion.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Hybridizing Machine Learning and Physically-based Earth System Models to Improve Prediction of Multivariate Extreme Events (AI Exploration of Wildland Fire Prediction)

Focal Areas: This project responds to two focal areas identified in the DOE Call for AI4ESP White Papers: 1) Predictive modeling through the use of artificial intelligence (AI) techniques, and 2) insights gleaned from complex data using explainable AI and big data analytics. Science Challenge: Large wildland fires (hereafter wildfires) appearing as high-impact compound climate extreme events are closely related to hydroclimate and water cycle extremes that modulate surface fuel supply and combustibility. These compound events have multivariate climatic features (e.g., temperature, precipitation, relative humidity, wind, lightning) and societal drivers (e.g., forest management, land use change, human caused ignitions). Meanwhile, they induce strong feedbacks to the coupled atmosphere, biosphere, and hydrosphere by perturbing regional and global radiation budget as well as ecological, biogeochemical, and water cycles across multiple spatiotemporal scales. The nonlinear interactions between these natural and anthropogenic components of the Earth system are too complex to be completely and adequately represented in today’s Earth system models (ESMs). The inherent stochastic nature of fire activity at all scales further increases the difficulty of its prediction using ESMs that are usually developed from deterministic equations and parameterizations. Besides, concurrence of long-term (decadal to interdecadal) global climate change and fire regime shifts overlapping with short-term (intraseasonal to interannual) variations of regional fire weather and burning activity confound predictability of these compound extreme events. We propose to address the above scientific challenges by using machine learning (ML)-based data-driven modeling techniques to integrate observations and physically-based ESMs’ simulations in a computationally efficient hybrid prediction system. This prediction system is supposed to characterize the wildfire’s sensitivity to climate and exogenous drivers at high resolution (~ 0.25°) on subseasonal to seasonal (S2S) timescales providing improved predictability and explainability. We will use the system to help identify: (1) What are the computational elements of a hybrid system needed to predict compound climate extreme events such as global wildfires? (2) What are the key drivers (either natural or anthropogenic) that modulate short-term variations of multivariate fire weather and burning activity over different regions? How can one take advantage of those driver-response relationships to improve the predictability of large wildfires on S2S time scales? (3) What are the underlying physical mechanisms and sources of improved predictability? Which ML techniques are optimal in revealing and adapting these mechanisms?

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Consequences of sludge composition on combustion performance derived from thermogravimetry analysis

Highlights: • Volatiles, particularly proteins, play a key role in sludge combustion. • Sludge combustion performance varies with different sludge organic concentrations. • Carbohydrates significantly affect the combustion rate in the second stage. • Combustion performance of digested sludge is more negative compared with others. - Abstract: Wastewater treatment plants produce millions of tons of sewage sludge. Sewage sludge is recognized as a promising feedstock for power generation via combustion and can be used for energy crisis adaption. We aimed to investigate the quantitative effects of various sludge characteristics on the overall sludge combustion process performance. Different types of sewage sludge were derived from numerous wastewater treatment plants in Beijing for further thermogravimetric analysis. Thermogravimetric–differential thermogravimetric curves were used to compare the performance of the studied samples. Proximate analytical data, organic compositions, elementary composition, and calorific value of the samples were determined. The relationship between combustion performance and sludge composition was also investigated. Results showed that the performance of sludge combustion was significantly affected by the concentration of protein, which is the main component of volatiles. Carbohydrates and lipids were not correlated with combustion performance, unlike protein. Overall, combustion performance varied with different sludge organic composition. The combustion rate of carbohydrates was higher than those of protein and lipid, and carbohydrate weight loss mainly occurred during the second stage (175–300 °C). Carbohydrates have a substantial effect on the rate of system combustion during the second stage considering the specific combustion feature. Additionally, the combustion performance of digested sewage sludge is more negative than the others.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

9Cr steel visualization and predictive modeling

The goals of this work were to develop tools to visualize and characterize materials information, to explore new alloy compositions and/or processing, to better predict tensile strength and other mechanical properties. The 9–12 wt% Cr (9Cr) steel test data were compiled from several sources. About 3000 records with 30 predictor variables represent 82 unique steel alloy compositions, variations of thermo-mechanical processing steps and temperatures (homogenization, normalization and tempering cycles), test conditions and outcomes. Detailed data processing steps such as visualization and exploratory analysis, including univariate and bivariate analysis, different clustering techniques used to segment the data, statistical post-hoc analysis to verify significance of the findings, feature engineering to identify predictors of importance, and predictive modeling with cross-validation were performed. The outcome of analysis at each step was reviewed in the context of the domain knowledge of this class of steel, to see if there were underlying physical mechanisms that explain statistical relationships. Data analytics techniques and their parameters were fine-tuned to facilitate interpretation of the results as aligned with the insights from domain experts. Here, the ensemble predictive modeling using Random Forest regressor and post-hoc means comparison corroborated domain knowledge on the role of Co which is known to increase strength of steel alloys through solid-solution strengthening and to affect diffusion of alloying elements and precipitates. Alloys with Co content of 0.7–8 wt% had significantly higher mean strength than the ones without (while having Cr locked within 10–11 wt% range in either group and keeping other compositional element ratios fixed). End products of the computational techniques exploration are presented as the tools that can be used in iterative workflow of materials development and testing.

36 MATERIALS SCIENCE↗
DOE OSTI2018

A large-scale evaluation of automated metadata inference approaches on sensors from air handling units

Building automation systems provide abundant sensor data to enable the potential of using data analytics to, among other things, improve the energy efficiency of the building. However, deployment of these applications for buildings, such as, fault detection and diagnosis (FDD) on multiple buildings remains a challenge due to the non-trivial efforts of organizing, managing and extracting metadata associated with sensors (e.g., information about their location, function, etc.), which is required by applications. One of the reasons leading to the problem is that varying conventions, acronyms, and standards are used to define this metadata. To better understand the nature of the problem, as well as the performance and scalability of existing solutions, in this report we implement and test 6 different time-series based metadata inference approaches on sensors from 614 air handling units (AHU) instrumented in 35 building sites accounting for more than 400 buildings distributed across United States of America. We infer 12 types of sensors and actuators in AHUs required by a rule-based FDD application: AHU performance and assessment rules (APAR). Our results show that: (1) the average performance of these approaches in terms of accuracy is similar across building sites, though there is significant variance; (2) the expected accuracy of classifying the type of points required by APAR for a new unseen building is, on average, 75%; (3) the performance of the model does not decrease as long as training data and testing data are extracted from adjacent months.

42 ENGINEERING↗
DOE OSTI2024

Influence of tritium exposure route on vegetation types at the Savannah River Site

Plant, soil, water, and other media from various locations at the Savannah River Site were measured for total tritium (T) content and T speciation to characterize T in these areas, as well as investigate its uptake behavior and the transport of T species in these media. This characterization included the isolation and measurement of T in tritiated water (HTO), and (when possible) exchangeable organic bound T (E-OBT) and non-exchangeable organic bound T (NE-OBT). Two areas of interest were investigated: (1) a holding pond with T-contaminated water and (2) open basins or streams with low to background levels of T. Water in the holding pond is used to irrigate forest plots in the local area as a T remediation approach. This study compares the analytical data for water, soil/sediment, plants, and lichens from these locations. Here, the results indicate that the behavior of T in plants from these areas can be a function of one or more of the following: seasonal precipitation, the plant's primary route of access to the T-contamination (such as water uptake through the root vs. shoot), plant physical location (relative to T-contaminated water sources), plant rooting depth, pond water level, and plant height above the ground. Total T concentrations were lowest in the un-irrigated forest plants, followed by irrigated forest plants, shallow rooting plants near the pond, deep rooting plants further from the pond, and then water-saturated plants. The OBT:HTO and NE-OBT:E-OBT ratios were always greater for plants from irrigated forest plots compared to those from the holding pond.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Condition Monitoring of Wind Turbine Planetary Gearboxes Under Different Operating Conditions

Digitally enhanced services for wind power could reduce Operations and Maintenance (O&M) costs as well as the Levelised Cost Of Energy (LCOE). Therefore, there is a continuous need for advanced monitoring techniques which can exploit the opportunities of Internet of Things (IoT) and Big Data Analytics, revolutionizing the future of the energy sector. The heart of wind turbines is a rather complex epicyclic gearbox. Among others, extremely critical gearbox components which are often responsible for machinery stops are the rolling element bearings. The vibration signatures of bearings are rather weak compared to other components, such as gears, and as a result an extended number of signal processing techniques and tools have been proposed during the last decades, focusing towards accurate, early, and on time bearing fault detection with limited false alarms and missed detections. Envelope Analysis is one of the most important methodologies, where an envelope of the vibration signal is estimated usually after filtering around a frequency band excited by impacts due to the bearing faults. Different tools, such as Kurtogram, have been proposed in order to accurately select the optimum filter parameters (center frequency and bandwidth). Cyclic Spectral Correlation and Cyclic Spectral Coherence, based on Cyclostationary Analysis, have been proved as very powerful tools for condition monitoring. The monitoring techniques seem to have reached a mature level in case a machinery operates under steady speed and load. On the other hand, in case the operating conditions change, it is still unclear whether the change of the monitoring indicators is due to the change of the health of the machinery or due to the change of the operating parameters. Recently, the authors have proposed a new tool called IESFOgram, which is based on Cyclic Spectral Coherence and can automatically select the filtering band. Furthermore, the Cyclic Spectral Coherence is integrated along the selected frequency band leading to an Improved Envelope Spectrum. In this paper, the performance of the tool is evaluated and further extended on cases operating under different speeds and different loads. The effectiveness of the methodology is tested and validated on the National Renewable Energy Laboratory (NREL) wind turbine gearbox vibration condition monitoring benchmarking data set which includes various faults with different levels of diagnostic complexity as well as various speed and load operating conditions.

17 WIND ENERGY↗
DOE OSTI2024

Multicode benchmark on simulated Ti K-edge x-ray absorption spectra of Ti-O compounds

X-ray absorption spectroscopy (XAS) is an element-specific materials characterization technique that is sensitive to structural and electronic properties. First-principles simulated XAS has been widely used as a powerful tool to interpret experimental spectra and draw physical insights. Recently, there has also been growing interest in building computational XAS databases to enable data analytics and machine learning applications. However, there are non-trivial differences among commonly used XAS simulation codes, both in underlying theoretical formalism and in technical implementation. Reliable and reproducible computational XAS databases require systematic benchmark studies. In this work, we benchmarked Ti K-edge XAS simulations of ten representative Ti-O binary compounds, which we refer to as the Ti-O-10 dataset, using three state-of-the-art codes: XSPECTRA, OCEAN and exciting. We systematically studied the convergence behavior with respect to the input parameters and developed a workflow to automate and standardize the calculations to ensure converged spectra. Our benchmark comparison considers a 35 eV spectral range starting from the K-edge onset, representative of widely used near-edge spectra. Quantitative comparison over this range is based on Spearman’s rank correlation score (r sp ). Our results show: (1) the two Bethe-Salpeter equation (BSE) codes (OCEAN and exciting) have excellent agreement with an average $r$ $sp$ of 0.998; (2) good agreement is obtained between the core-hole potential code (XSPECTRA) and BSE codes (OCEAN and exciting) with an average $r$ $sp$ of 0.990, and this smaller $r$ $sp$ reflects the noticeable differences in the main edge spectral shape that can be primarily attributed to the difference in the strength of the screened core-hole potential; (3) simulations from both methods overall reproduce well the main experimental spectral features of rutile and anatase, and the different treatments of the screened core-hole potential have visible impact on pre-edge intensities and the peak ratio of the main edge; (4) there exist moderate differences in the relative edge alignment of the three codes with a standard deviation of about 0.2 eV, which arise from multiple contributions including the frozen core approximation, final state effects, and different approximations used for the self-energy correction. In conclusion, our benchmark study provides important standards for first-principles XAS simulations with broad impact in data-driven XAS analysis.

36 MATERIALS SCIENCE↗
DOE OSTI2016

A Business Case for Nuclear Plant Control Room Modernization

This paper presents a generic business case for implementation of technology that supports Control Room Modernization (CRM). The analysis presented in two forms; 1) a standalone technology upgrade, and 2) a technology upgrade that is built upon and incremental to a prior business case created for Mobile Work Packages (MWP). The business case contends that advanced communication and networking and analytical technologies will allow NPP to conduct control room operations with improved focus by reducing human factors and redundant manpower, and therefore operate with fewer errors. While some labor savings can be harvested in terms of overtime, the majority of savings are demonstrated as reduced time to take the plant off line and bring back on line in support of outages. The benefits are quantified to a rough order of magnitude that provides directional guidance to NPPs that are interested in developing a similar business case. This business case focuses on modernization of the operator control room and does not consider a complete overhaul and modernization of a plants instrument and control systems. While operators may be considering such an investment at their plants, the sizable capital investment required is not likely supported by a cost/benefit analysis alone. More likely, it is driven by obsolescence and reliability issues, and requires consideration of mechanical condition of plant systems, capital depreciation, financing, relicensing and overall viability of the plant asset over a 20-year horizon in a competitive market. Prior studies [REF] have indicated that such a modernization of plant I&C systems, alone or as part of a larger modernization effort, can yield very significant reductions in O&M costs. However, the depth of research and analysis required to develop a meaningful business case for a plant modernization effort is well beyond the scope of this study. While CRM as considered in this study can be easily integrated as part of grander plant modernization effort, it can also be considered as a stand-alone project, implemented as a supervisorial layer over and above existing systems. CRM is enabled by a suite of technologies, which are described in further detail in the body of this report. They include: • Modernized control room with interactive displays • High-bandwidth wireless networks • Mobile devices • Component identification technology • Mobile wireless video cameras • Smart or task based operator displays • Computer-Based Procedures (CBP)/Automated Mobile Work Packages (MWP) • Intelligent plant configuration • Advanced data analytics An analysis was conducted to determine how these technologies might impact outage operations at an NPP. The analysis concluded that outage management capabilities would be improved with application of these technologies, and result in annual benefits for the plant. These improved capabilities were identified and described in further detail in this report. • Reduction in need redundant manpower (verification, etc) • Improved critical path shutdown and restart duration during outages • Reduction in human factors and errors • Remote watches • Paperless outage coordination • Bulk work optimization

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Scalable Pattern Matching in Metadata Graphs via Constraint Checking

Pattern matching is a fundamental tool for answering complex graph queries. Unfortunately, existing solutions have limited capabilities: They do not scale to process large graphs and/or support only a restricted set of search templates or usage scenarios. Moreover, the algorithms at the core of the existing techniques are not suitable for today’s graph processing infrastructures relying on horizontal scalability and shared-nothing clusters, as most of these algorithms are inherently sequential and difficult to parallelize. In this article we present an algorithmic pipeline that bases pattern matching on constraint checking. The key intuition is that each vertex and edge participating in a match has to meet a set of constraints implicitly specified by the search template. These constraints can be verified independently and typically are less expensive to compute than searching the full template. The pipeline we propose generates these constraints and iterates over them to eliminate all the vertices and edges that do not participate in any match, thus reducing the background graph to a subgraph that is the union of all template matches—the complete set of all vertices and edges that participate in at least one match. Additional analysis can be performed on this annotated, reduced graph, such as full match enumeration, match counting, or computing vertex/edge centrality. Furthermore, a vertex-centric formulation for constraint checking algorithms exists, and this makes it possible to harness existing high-performance, vertex-centric graph processing frameworks. This technique (i) enables highly scalable pattern matching in metadata (labeled) graphs; (ii) supports arbitrary patterns with 100% precision; (iii) enables tradeoffs between precision and time-to-solution, while always selects all vertices and edges that participate in matches, thus offering 100% recall; and (iv) supports a set of popular data analytics scenarios. We implement our approach on top of HavoqGT, an open-source asynchronous graph processing framework, and demonstrate its advantages through strong and weak scaling experiments on massive scale real-world (up to 257 billion edges) and synthetic (up to 4.4 trillion edges) labeled graphs, respectively, and at scales (1,024 nodes / 36,864 cores), orders of magnitude larger than used in the past for similar problems. This article serves two purposes: First, it synthesises the knowledge accumulated during a long-term project. Second, it presents new system features, usage scenarios, optimizations, and comparisons with related work that strengthen the confidence that pattern matching based on iterative pruning via constraint checking is an effective and scalable approach in practice. The new contributions include the following: (i) We demonstrate the ability of the constraint checking approach to efficiently support two additional search scenarios that often emerge in practice, interactive incremental search and exploratory search. (ii) We empirically compare our solution with two additional state-of-the-art systems, Arabsque and TriAD. (iii) We show the ability of our solution to accommodate a more diverse range of datasets with varying properties, e.g., scale, skewness, label distribution, and match frequency. (iv) We introduce or extend a number of system features (e.g., work aggregation, load balancing, and the ability to cap the generated traffic) and design optimizations and demonstrate their advantages with respect to improving performance and scalability. (v) We present bottleneck analysis and insights into artifacts that influence performance. (vi) We present a theoretical complexity argument that motivates the performance gains we observe.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Recent Advances in Machine Learning for Fiber Optic Sensor Applications

Over the last three decades, fiber optic sensors (FOS) have gained a lot of attention for their wide range of monitoring applications across many industries, including aerospace, defense, security, civil engineering, and energy. FOS technologies hold great promise to form the backbone for next‐generation intelligent sensing platforms that offer long‐distance, high‐accuracy, distributed measurement capabilities and multiparametric monitoring with resilience to harsh environmental conditions. The major limitations posed by FOS are 1) cross‐sensitivity, 2) enormous volume and large data generation, 3) low data processing speed, 4) degradation of signal‐to‐noise ratio over the fiber length, and 5) overall cost of sensor and interrogator systems. These challenges can be overcome by building advanced data analytics engines enabled by recent breakthroughs in machine learning (ML) and artificial intelligence (AI). This article presents a comprehensive review of recent studies that integrate ML and AI algorithms with FOS technologies. This review also highlights several FOS technology development directions that promise a significant impact on widespread use for several industrial applications, with an emphasis on energy systems monitoring. A perspective on future directions for further research development is also provided.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Materials Engineering of Violin Soundboards by Stradivari and Guarneri

Abstract We investigated the material properties of Cremonese soundboards using a wide range of spectroscopic, microscopic, and chemical techniques. We found similar types of spruce in Cremonese soundboards as in modern instruments, but Cremonese spruces exhibit unnatural elemental compositions and oxidation patterns that suggest artificial manipulation. Combining analytical data and historical information, we may deduce the minerals being added and their potential functions—borax and metal sulfates for fungal suppression, table salt for moisture control, alum for molecular crosslinking, and potash or quicklime for alkaline treatment. The overall purpose may have been wood preservation or acoustic tuning. Hemicellulose fragmentation and altered cellulose nanostructures are observed in heavily treated Stradivari specimens, which show diminished second‐harmonic generation signals. Guarneri's practice of crosslinking wood fibers via aluminum coordination may also affect mechanical and acoustic properties. Our data suggest that old masters undertook materials engineering experiments to produce soundboards with unique properties.

Su, Cheng‐Kuan↗
DOE OSTI2018

Eigensolver performance comparison on Cray XC systems

Summary Hermitian (symmetric) eigenvalue solvers are the core constituents of electronic structure, quantum‐chemistry, and other HPC applications such as Quantum ESPRESSO, VASP, CP2K, and NWChem to name a few. Our understanding of the performance of symmetric eigenvalue algorithms on various hardware is clearly important to the quantum chemistry or condensed matter physics community but in fact goes beyond that community. For instance, big data analytics is increasingly utilizing eigenvalues solvers, in the study of randomized singular value decomposition (SVD) or principal component analysis (PCA). Noise, vibration, and harshness (NVH) is another field where fast and efficient eigenvalue solvers are required. Most eigenvalue solver packages feature numerous different parameters which can be tuned for performance, eg, the number of nodes, number of total ranks, the decomposition of the matrix, etc. In this paper, we investigate the performance of different packages as well as the influence of these knobs on the solver performance.

Cook, Brandon↗
DOE OSTI2023

Extending OpenSHMEM with Aggregation Support for Improved Message Rate Performance

OpenSHMEM is a highly efficient one-sided communication API that implements the PGAS parallel programming model, and is known for its low latency communication operations that can be mapped efficiently to RDMA capabilities of network interconnects. However, applications that use OpenSHMEM can be sensitive to point-to-point message rates, as many-to-many communication patterns can generate large amounts of small messages which tend to overwhelm network hardware that has predominantly been optimised for bandwidth over message rate. Additionally, many important emerging classes of problems such as data analytics are similarly troublesome for the irregular access patterns they employ. Message aggregation strategies have been proven to significantly enhance network performance, but their implementation often involves complex restructuring of user code, making them unwieldy. This paper shows how to combine the best qualities of message aggregation within the communication model of OpenSHMEM such that applications with small and irregular access patterns can improve network performance while maintaining their algorithmic simplicity. We do this by providing a path to a message aggregation framework called conveyors through a minimally intrusive OpenSHMEM extension introducing aggregation contexts that fit more naturally to the OpenSHMEM atomics, gets, and puts model. We test these extensions using four of the bale 3.0 applications which contain essential many-to-many access patterns to show how they can produce performance improvements of up to 65×.

Welch, Aaron↗
DOE OSTI2018

Evaluating On-Node GPU Interconnects for Deep Learning Workloads

Scaling deep learning workloads across multiple GPUs on a single node has become increasingly important in data analytics. A key question is how well a PCIe-based GPU interconnect can perform relative to a custom high-performance interconnect such as NVIDIA's NVLink. This paper evaluates two such on-node interconnects for eight NVIDIA Pascal P100 GPUs: (a) the NVIDIA DGX-1's NVLink 1.0 `hybrid cube mesh'; and (b) the Cirrascale GX8's two-level PCIe tree using dual SR3615 switch risers. To show the effects of a range of neural network workloads, we define a parameterized version of the popular ResNet. We define a workload intensity metric that characterizes the expected computation/communication ratio; we also locate AlexNet and GoogLeNet within that space. As expected, the DGX-1 typically has superior performance. However, when equalizing GPU SM frequencies, the GX8 is very competitive on all ResNet workloads. With 8 GPUs, the GX8 can outperform the DGX-1 on all-to-all reductions by 10% for medium-sized payloads; and in rare cases, the GX8 slightly outperforms on ResNet.

Tallent, Nathan R.↗
DOE OSTI2018

Additive Manufacturing of Nickel Superalloys: Opportunities for Innovation and Challenges Related to Qualification

Innovative designs for turbines can be achieved by advances in nickel-based superalloys and manufacturing methods, including the adoption of additive manufacturing. In this regard, selective electron beam melting (SEBM) and selective laser melting (SLM) of nickel-based superalloys do provide distinct advantages. Furthermore, the direct energy deposition (DED) processes can be used for repair and reclamation of nickel alloy components. The current paper explores opportunities for innovation and qualification challenges with respect to deployment of AM as a disruptive manufacturing technology. Here in the first part of the paper, fundamental correlations of processing parameters to defect tendency and microstructure evolution will be explored using DED process. In the second part of the paper, opportunities for innovation in terms of site-specific control of microstructure during processing will be discussed. In the third part of the paper, challenges in qualification of AM parts for service will be discussed and potential methods to alleviate these issues through in situ process monitoring, and big data analytics are proposed.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Nitric-acid hydrolysis of Miscanthus giganteus to sugars fermented to bioethanol

Miscanthus giganteus ( M. giganteus ) is a promising feedstock for the production of bioethanol or biochemicals. Using only dilute nitric acid, this work describes a two-step process for hydrolyzing hemicellulose and cellulose to fermentable sugars. Primary variables were temperature and reaction time. The solid-to-liquid mass ratio was 1:8. No enzymes were used. In the first step, M. giganteus was contacted with 0.5 wt.% nitric acid at temperatures between 120 and 160°C for 5 to 40 min. The second step used 0.5 or 0.75 wt.% nitric acid at temperatures between 180 and 210°C for less than 6 min. Under selected conditions, almost all hemicellulose and 58% cellulose were transferred to the liquid phase. Small amounts of degradation products were observed. The xylose solution obtained from the nitric-acid hydrolysis was fermented for 96 h and the glucose solution for 48 h to yield 0.41 g ethanol/g xylose and 0.46 g ethanol/g glucose. To characterize residual solids and the liquor from both steps, nuclear-magneticresonance (NMR) spectroscopy was performed for each fraction. In conclusion, the analytical data indicate that the liquid phase from Steps 1 and 2 contain little lignin or lignin derivatives.

09 BIOMASS FUELS↗