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DOE OSTI2020

Scalable and Actionable Performance Measures for Traffic Signal Systems using Probe Vehicle Trajectory Data

Scalable and actionable performance measures for traffic signal systems provide opportunities for practitioners to measure and improve the transportation network. Historically, traffic signal improvements have relied on scheduled signal retiming based on limited data collection, or on the public to call and alert engineers of an issue. This inefficient method of improving signal timing led to the creation of automated traffic signal performance measures (ATSPMs). These metrics rely on expensive infrastructure, including detection and communications, which has produced barriers for numerous agencies to fully adopt. Recently, third-party data providers have begun to release vehicle trajectory data, which allows for enhanced signal metrics with no investment in physical equipment. The purpose of this study is to demonstrate the use of these data and summarize the scalability of the created metrics. This work builds on previous efforts to quantify signal performance on nine intersections in Michigan, U.S. Ten signalized corridors in Columbus, Ohio, were chosen to scale a performance assessment using crowdsourced trajectory data. A total of 136 intersections were assessed in 2-h intervals using data from all weekdays in 2017. High-level corridor summary metrics including average percent of vehicles stopping (18%–32%), average delay (9.4–20.5 s), and level of travel time reliability (1.23–2.73) were calculated for each corridor direction. Intersection-level metrics were also introduced, which can be used by practitioners to identify problems, improve signal timings, and prioritize future infrastructure investments.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Scalable multilevel Monte Carlo methods exploiting parallel redistribution on coarse levels

Here, we study an element agglomeration coarsening strategy that requires data redistribution at coarse levels when the number of coarse elements becomes smaller than the number of MPI processes used on the finest level. The overall procedure generates coarse elements (general unstructured unions of fine grid elements) within the framework of element-based algebraic multigrid methods (or AMGe) studied previously. The AMGe-generated coarse spaces have the ability to exhibit approximation properties of the same order as the fine-level spaces since by construction they contain the piecewise polynomials of the same order as on the fine level. These approximation properties are key for the successful use of AMGe in multilevel solvers for nonlinear partial differential equations as well as for multilevel Monte Carlo (MLMC) simulations. The ability to coarsen without being constrained by the number of MPI processes, as described in the present paper, allows to improve the scalability of these solvers as well as the overall MLMC method. The paper illustrates this latter fact with detailed scalability study of MLMC simulations applied to model Darcy equations with a stochastic log-normal permeability field.

AMGe↗

DOE OSTI2019

DECA: scalable XHMM exome copy-number variant calling with ADAM and Apache Spark

Abstract Background XHMM is a widely used tool for copy-number variant (CNV) discovery from whole exome sequencing data but can require hours to days to run for large cohorts. A more scalable implementation would reduce the need for specialized computational resources and enable increased exploration of the configuration parameter space to obtain the best possible results. Results DECA is a horizontally scalable implementation of the XHMM algorithm using the ADAM framework and Apache Spark that incorporates novel algorithmic optimizations to eliminate unneeded computation. DECA parallelizes XHMM on both multi-core shared memory computers and large shared-nothing Spark clusters. We performed CNV discovery from the read-depth matrix in 2535 exomes in 9.3 min on a 16-core workstation (35.3× speedup vs. XHMM), 12.7 min using 10 executor cores on a Spark cluster (18.8× speedup vs. XHMM), and 9.8 min using 32 executor cores on Amazon AWS’ Elastic MapReduce. We performed CNV discovery from the original BAM files in 292 min using 640 executor cores on a Spark cluster. Conclusions We describe DECA’s performance, our algorithmic and implementation enhancements to XHMM to obtain that performance, and our lessons learned porting a complex genome analysis application to ADAM and Spark. ADAM and Apache Spark are a performant and productive platform for implementing large-scale genome analyses, but efficiently utilizing large clusters can require algorithmic optimizations and careful attention to Spark’s configuration parameters.

Linderman, Michael D. (ORCID:0000000296437148)↗

DOE OSTI2022

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules

Abstract Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures. Training an accurate and comprehensive GCNN surrogate for molecular design requires large-scale graph datasets and is usually a time-consuming process. Recent advances in GPUs and distributed computing open a path to reduce the computational cost for GCNN training effectively. However, efficient utilization of high performance computing (HPC) resources for training requires simultaneously optimizing large-scale data management and scalable stochastic batched optimization techniques. In this work, we focus on building GCNN models on HPC systems to predict material properties of millions of molecules. We use HydraGNN, our in-house library for large-scale GCNN training, leveraging distributed data parallelism in PyTorch. We use ADIOS, a high-performance data management framework for efficient storage and reading of large molecular graph data. We perform parallel training on two open-source large-scale graph datasets to build a GCNN predictor for an important quantum property known as the HOMO-LUMO gap. We measure the scalability, accuracy, and convergence of our approach on two DOE supercomputers: the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF) and the Perlmutter system at the National Energy Research Scientific Computing Center (NERSC). We present our experimental results with HydraGNN showing (i) reduction of data loading time up to 4.2 times compared with a conventional method and (ii) linear scaling performance for training up to 1024 GPUs on both Summit and Perlmutter.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

High-temperature, spectrally-selective, scalable, and flexible thin-film Si absorber and emitter

Solar thermal technologies have great potential to provide low-cost storage for solar energy. However, their efficiencies are limited by a lack of scalable, mechanically flexible, durable, yet highly-efficient spectrally-selective solar absorbers suitable for high temperatures at low solar concentrations. Here, we overcome these challenges by fabricating a scalable free-standing spectrally-selective thin-film Si absorber and emitter (SSTFS) composite. Its high-temperature emittance shows strong spectral selectivity, even at 595 °C. Thermal stability is proven by measuring optical properties before and after thermal cycling equivalent to one day of concentrated sunlight. Despite the use of crystalline Si, the fabricated SSTFS composite exhibits exceptional mechanical flexibility to cover most surface geometries. The SSTFS composite demonstrates the potential of high-temperature, efficient and flexible solar absorbers and thermal emitters to advance renewable solar energy with storage.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Scalable low-loss cryogenic packaging of quantum memories in CMOS-foundry processed photonic chips

Optically linked solid-state quantum memories such as color centers in diamond are a promising platform for distributed quantum information processing and networking. Photonic integrated circuits (PICs) have emerged as a crucial enabling technology for these systems, integrating quantum memories with efficient electrical and optical interfaces in a compact and scalable platform. Packaging these hybrid chips into deployable modules while maintaining low optical loss and resiliency to temperature cycling is a central challenge to their practical use. We demonstrate a packaging method for PICs using surface grating couplers and angle-polished fiber arrays that is robust to temperature cycling, offers scalable channel count, applies to a wide variety of PIC platforms and wavelengths, and offers pathways to automated high-throughput packaging. Using this method, we show optically and electrically packaged quantum memory modules integrating all required qubit controls on chip, operating at millikelvin temperatures with <3 dB losses achievable from fiber to quantum memory for the TE 0 mode at a wavelength of 737 nm.

Bernson, Robert [Tyndall National Institute, Cork ↗

DOE OSTI2018

Towards Operational Scalability for H⁻ Laser Assisted Charge Exchange

The experimental development of H⁻ laser assisted charge exchange, a.k.a. laser stripping, has been ongoing at the SNS accelerator since 2006 in a three-phase approach. The first two phases associated with proof-of-principle and proof-of-practicality experiments have been successfully completed and demonstrated >95% H⁻ stripping efficiency for up to 10 us. The final phase is a proof-of-scalability stage to demonstrate that the method can be deployed for realistic beam duty factors. The experimental component of this effort is centered on achieving high efficiency stripping through the use of a laser power amplification scheme to recycle the macropulse laser light at the interaction point of the H⁻ stripping. Such a recycling cavity will be necessary for any future operational laser stripping system with at least millisecond duration H⁻ pulses. A second component of the proof-of-scalability phase is to develop a conceptual design for a realistic laser stripping scheme. The status of these efforts and challenges associated with deploying the recycling cavity into the laser stripping experiment will be described in this talk.

43 PARTICLE ACCELERATORS↗

DOE OSTI2014

Architecture-Aware Algorithms for Scalable Performance and Resilience on Heterogeneous Architectures. Final Report

With the coming end of Moore's law, it has become essential to develop new algorithms and techniques that can provide the performance needed by demanding computational science applications, especially those that are part of the DOE science mission. This work was part of a multi-institution, multi-investigator project that explored several approaches to develop algorithms that would be effective at the extreme scales and with the complex processor architectures that are expected at the end of this decade. The work by this group developed new performance models that have already helped guide the development of highly scalable versions of an algebraic multigrid solver, new programming approaches designed to support numerical algorithms on heterogeneous architectures, and a new, more scalable version of conjugate gradient, an important algorithm in the solution of very large linear systems of equations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Techno-Economic Analysis of Scalable Coal-Based Fuel Cells

Researchers at The University of Akron (UA) have demonstrated the technical feasibility of a laboratory coal fuel cell that can economically convert high sulfur coal into electricity with near zero negative environmental impact. Scaling up this coal fuel cell technology to the megawatt scale for the nation’s electric power supply requires two key elements: (i) developing the manufacturing technology for the components of the coal-based fuel cell, and (ii) long term testing of a kW scale fuel cell pilot plant. This project was expected to develop a scalable coal fuel cell manufacturing process through testing, demonstrating the feasibility of building a large-scale coal fuel cell power plant. We have developed a reproducible tape casting technique for the mass production of the planner fuel cells. Low cost interconnect and cathode current collector material was identified and current collection was improved. In addition, this study has demonstrated that electrochemical oxidation of carbon can take place on the Ni anode surface and the CO and CO 2 product produced can further react with carbon to initiate the secondary reactions. One important secondary reaction is the reaction of carbon with CO 2 to produce CO. We found CO and carbon can be electrochemically oxidized simultaneously inside of the anode porous structure and on the surface of anode for producing electricity. Since CH 4 produced from coal during high temperature injection of coal into the anode chamber can cause severe deactivation of Ni-anode, we have studied how CH 4 can interact with CO 2 to produce in the anode chamber. CO produced was found able to inhibit coking and allow the rate of anode deactivation to be decreased. An injection system was developed to inject the solid carbon and coal fuels without bringing air into the anode chamber. Five planner fuel cells connected in a series configuration and tested. Extensive studies on the planner fuels and stack revealed that the planner fuel cell stack is not suitable for operation with carbon and coal fuels due to lack of mechanical strength and difficulty in sealing. We have developed scalable processes for manufacturing of process for planner and tubular cells. Our studies suggested that tubular cell stack could be the only option for scaling up the coal-based fuel cell. Although the direct feeding of coal into fuel cell can significantly simplify the fuel cell system, the durability of the fuel cell needs to be further improved before scaling up. We are developing a tubular fuel cell stack with a coal injection and a CO 2 recycling unit.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2015

Enabling Secure Scalable Microgrids with High Penetration Renewables

Laboratory Directed Research and Design (LDRD) originating technologies are being developed to address challenges inherent to highly stochastic energy sources and loads, to conceivably satisfy the electrical energy needs of national/international power systems. The Enabling Secure, Scalable Microgrids with High Penetration Renewables Grand Challenge LDRD (FY11-FY13) aimed to develop a novel intelligent grid architecture, Secure Scalable Microgrid (SSM), based on closed loop controls and an agent-based architecture supporting intelligent power flow control. The approach was to enable self-healing, self-adapting, self-organizing architectures and allow a trade-off between storage in the grid versus information flow to control generation sources, power distribution, and where necessary, loads. L

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2017

Scalable Metadata Management for a Large Multi-Source Seismic Data Repository

In this work, we implemented the key metadata management components of a scalable seismic data ingestion framework to address limitations in our existing system, and to position it for anticipated growth in volume and complexity. We began the effort with an assessment of open source data flow tools from the Hadoop ecosystem. We then began the construction of a layered architecture that is specifically designed to address many of the scalability and data quality issues we experience with our current pipeline. This included implementing basic functionality in each of the layers, such as establishing a data lake, designing a unified metadata schema, tracking provenance, and calculating data quality metrics. Our original intent was to test and validate the new ingestion framework with data from a large-scale field deployment in a temporary network. This delivered somewhat unsatisfying results, since the new system immediately identified fatal flaws in the data relatively early in the pipeline. Although this is a correct result it did not allow us to sufficiently exercise the whole framework. We then widened our scope to process all available metadata from over a dozen online seismic data sources to further test the implementation and validate the design. This experiment also uncovered a higher than expected frequency of certain types of metadata issues that challenged us to further tune our data management strategy to handle them. Our result from this project is a greatly improved understanding of real world data issues, a validated design, and prototype implementations of major components of an eventual production framework. This successfully forms the basis of future development for the Geophysical Monitoring Program data pipeline, which is a critical asset supporting multiple programs. It also positions us very well to deliver valuable metadata management expertise to our sponsors, and has already resulted in an NNSA Office of Defense Nuclear Nonproliferation commitment to a multi-year project for follow-on work.

58 GEOSCIENCES↗

DOE OSTI2017

Scalability of Several Asynchronous Many-Task Models for In Situ Statistical Analysis.

This report is a sequel to [PB16], in which we provided a first progress report on research and development towards a scalable, asynchronous many-task, in situ statistical analysis engine using the Legion runtime system. This earlier work included a prototype implementation of a proposed solution, using a proxy mini-application as a surrogate for a full-scale scientific simulation code. The first scalability studies were conducted with the above on modestly-sized experimental clusters. In contrast, in the current work we have integrated our in situ analysis engines with a full-size scientific application (S3D, using the Legion-SPMD model), and have conducted nu- merical tests on the largest computational platform currently available for DOE science ap- plications. We also provide details regarding the design and development of a light-weight asynchronous collectives library. We describe how this library is utilized within our SPMD- Legion S3D workflow, and compare the data aggregation technique deployed herein to the approach taken within our previous work.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

A Scalable HPC Insider Threat Monitoring System

Cyber Equations, Inc. is reporting on the results of its DOE SBIR Phase I project for the development of a Scalable HPC Insider Threat Monitoring System. This final report to the Department of Energy for Task DE-FOA-0001618 covers the period from 02/21/2017 to 02/20/2018 and summarizes the larger research accomplishments, which can be divided in theoretical and laboratory experiments. One of the many challenges for the U.S. Department of Energy (DOE) is the support of the high-performance computing (HPC) centers while providing quality information services, strategic vision, leadership, technical expertise, and capabilities necessary to enable the DOE to deliver effective, efficient, economical, and secure HPC services. In order to realize a secure DOE laboratory enterprise, DOE facilities operations must counter a wide range of cyber insider threats, from unsophisticated adversaries up to advanced, nation state sponsored actors. The DOE laboratories also presents an enormous insider attack surface of an ever-growing number of computers, networks, and devices all running a myriad of software. In addition, the DOE laboratories are connected to a variety of external organizations which multiplies the possibility of insider threat. During this SBIR project Cyber Equations developed a strategic combination of supervised and unsupervised machine learning techniques to control scale and classify whether entities, such as hosts, actors, or documents, are showing suspicious insider behavior. We conducted experiments using real cyber data to show that changes in observable behavior (e.g., changes in outbound traffic patterns) are indicative of attacks executing (e.g., insiders exfiltrating data, bots calling home, or an attack spreading). The features and methods we use, have been used in the anomaly detection research and development community for a significant amount of time and robust implementations are openly available. K-Means clustering, Decision Trees, and Support Vector Machine based classifiers are readily available through the R language and open source H20 machine learning framework. The Cyber Equations approach is novel in its use of these classifiers combined with Hadoop based parallel processing technology. The five stages of processing used by CyberInsider improve scalability and reduce the high number of false positives normally associated with network anomaly detection. In summary, the Cyber Equations has successfully accomplished all of its Phase I tasks and demonstrated the feasibility of the Insider Threat Monitoring System.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Modeling Mesoscale Processes of Scalable Synthesis

Brown University is part of the Collaboratory on Mathematics for Mesoscopic Modeling of Materials (CM4), led by PNNL. The Collaboratory focuses on developing rigorous mathematical foundations for understanding and controlling fundamental mechanisms in mesoscale processes to enable scalable synthesis of complex materials, through the design of efficient modeling methods and corresponding scalable algorithms.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Microwave Irradiation Intensified Process for Scalable Functional Device Assembly

The environment pollution is a serious issue worldwide for human beings, which calls for ever-increasing demand towards environmental protection. Efficient enabling and utilization of various functional devices represent an essential solution to pursue the environment sustainability. Nanotechnology is emerging as an effective approach to improve the efficiency of functional devices for a variety of environmental applications. For example, the nanomaterials-based catalysts are being widely used in air pollution control, enabling catalysts with significantly enhanced performance. Scalable integration of functional nanostructural entities such as nanoparticles, nanowires, nanosheets into applicable three-dimensional (3-D) platform holds the keys of industrial-relevant manufacture of high-efficiency catalytic devices for emission control. Among all the integration approaches, the wet-chemical based integration is one of the top options due to its advantages in low-cost, flexibility in substrate geometry and composition, ease of operation, and high repeatability. The wet-chemical based processes usually require continuous heating to initiate the chemical reactions, which is regularly achieved by conventional resistive heating. However, such conventional heating method is plaqued by a poor heating efficiency. Moreover, the conventional heating is not suitable for those substrates with three-dimensional (3D) or complex geometry since it hardly provides sufficient heat transfer and mass transport within substrates, leading to poor uniformity of nanostructure deposition and low production rate. Thus, there is urgent demand to develop high-efficiency wet-chemical based integration strategy for nanostructured functional devices. In this project, a novel microwave-irradiation-intensified hydrothermal manufacturing process was developed for scalable manufacture of nanostructured monolithic catalytic devices. A wide array of metal oxide nanostructured coatings has been in situ grown on various 3D substrates for emission control applicaitons such as cordierite honeycombs and silicon carbide honeycomb. The growth efficiency, production rate, and distribution uniformity of nanostructure coatings obtained from microwave heating and conventional heating are investigated, which reveals the boosting effect of microwave irradiation on hydrothermal reactions. The silicon carbide honeycomb is found to be more favorable to microwave adsorption in contrast to cordierite honeycomb, and the corresponding reaction mechanism is studied. The obtained catalysts are used for catalytic oxidation of Volatile Organic Compounds (VOCs), showing excellent catalytic performance at low temperature for various compounds. As the proof of concept, the design of integrated manufacturing system for the microwave irradiation assisted continuous production of nanostructure array based monolithic catalysts is presented, which holds the key to mass production of monolithic catalysts in near future.

42 ENGINEERING↗

DOE OSTI2021

Developing a Low Cost, High Volume and Scalable Manufacturing Technology for Undoped and Heavily P-Type Doped CdTe Feedstock Materials (Final Report)

The goal of this project was to establish a cost-effective and scalable production approach of feedstock (CdTe and Cd-Se-Te) for the solar industry, with the material properties necessary to improve performance and reduce costs. The primary focus was to develop a CdTe synthesis and growth process that pushed the p-type doping of CdTe to previously unattainable levels, while creating a path to scalable production. In our approach, the first step was bulk CdTe growth doped with Group V dopants (P, As, and Sb) using the Modified Vertical Bridgman (MVB) and high Pressure Bridgman (HPB) techniques, and the second step was examination of films deposited by Close-Spaced Sublimation (CSS) and Vapor Transport Deposition (VTD) using this specialized feedstock.

14 SOLAR ENERGY↗

DOE OSTI2022

SMALE: Enhancing Scalability of Machine Learning Algorithms on Extreme-Scale Computing Platforms

Deployment and execution of machine learning tasks on extreme-scale computing platforms face several significant technical challenges: 1) High computing cost incurred by dense networks – The computing workload of deep networks with densely-connected topology increases rapidly with the network size, imposing a non-scalable computing model of extreme-scale computing platforms; 2) Non-optimized workload distribution – Many advanced deep learning algorithms, e.g., sparsification and irregular net-work topology, produce very unbalanced workload distribution on extreme-scale computing platforms. The computation efficiency is greatly hindered by the incurred data and computation redundancies as well as long tails of the node with extensive workload; 3) Constraints in data movement and I/O bottle-neck – Inter-node data movement in extreme-scale computing platforms are associated with high energy and latency costs, and subject to the constraints of I/O bandwidth; and 4) Generalization of algorithm realization and acceleration on computing platforms – The large varieties of machine learning algorithms and structures of extreme-scale computing platforms make the derivation of a generalized algorithm realization and acceleration method very challenging, which, however, is the requirement by domain scientists and interested users. We call the above challenges Smale’s Problems in Machine Learning and Understanding for High-Performance Computing Scientific Discovery. The objective of our three-year research project is to develop a holistic innovation set at structure, assembly, and acceleration layers of machine learning algorithms to address the above challenges in algorithm deployment and execution. Three tasks are particularly performed, including: At the algorithm structure level, we investigate the techniques that can structurally sparsify on the topology of deep networks for computing workload reduction. We also study clustering and pruning techniques that can optimize the workload distributions over the extreme-scale computing platforms; At the algorithm assembly level, we derive a unified learning framework for unsupervised transfer learning and dynamic growing capabilities. Novel training methods are also exploited to enhance the training efficiency of the proposed framework; At the algorithm acceleration level, we will develop a series of techniques that can accelerate the computation of sparse matrix operations, which are one of the core executions in deep learning and optimize memory access of the concerned platforms. Our proposed techniques attack the fundamental problems in machine learning algorithms running on extreme-scale computing platforms by vertically integrating the solutions at three closely entangled layers, paving the long-term scaling path of machine learning applications under DOE context. Three tasks corresponding to the above respective research orientations are performed during the three-year project period with our collaborators at ORNL. The outcome of the proposed project is anticipated to form a holistic solution set of novel algorithms and network topologies, efficient training techniques, and fast acceleration methods to promote the computing scalability of the machine learning applications of particular interest to DOE.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Scalable Approaches to Address Thermomechanical and Chemical Instabilities in Metal Halide Perovskites

We will discuss our group’s recent efforts to address the thermomechanical and chemical instabilities of metal halide perovskites through multiple approaches. These approaches cover several length scales and aim to provide better understanding of behavior in all stages of processing—i.e., precursor behavior in inks, growth behavior of films during printing, and resulting optoelectronic properties and robustness of perovskite films and devices. We will highlight our efforts toward scalable printing of high-quality perovskite using the method recently invented by our group restricted area printing by ink drawing (RAPID). Additionally, we will discuss how molecular additives in precursor inks can influence the stability of the inks and resultant films. Finally, we will also describe our recent progress in addressing the poor mechanical toughness of perovskite using multifunctional nanofiber reinforcements. Together, all of these concepts are complimentary and compatible with scalable printing of perovskites and a necessary step towards developing a viable technology with long operational lifetimes.

Printz, Adam↗