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DOE OSTI2020

The genome of jojoba ( Simmondsia chinensis ): A taxonomically isolated species that directs wax ester accumulation in its seeds

Seeds of the desert shrub, jojoba ( Simmondsia chinensis ), are an abundant, renewable source of liquid wax esters, which are valued additives in cosmetic products and industrial lubricants. Jojoba is relegated to its own taxonomic family, and there is little genetic information available to elucidate its phylogeny. Here, we report the high-quality, 887-Mb genome of jojoba assembled into 26 chromosomes with 23,490 protein-coding genes. The jojoba genome has only the whole-genome triplication (γ) shared among eudicots and no recent duplications. These genomic resources coupled with extensive transcriptome, proteome, and lipidome data helped to define heterogeneous pathways and machinery for lipid synthesis and storage, provided missing evolutionary history information for this taxonomically segregated dioecious plant species, and will support efforts to improve the agronomic properties of jojoba.

Sturtevant, Drew↗

DOE OSTI2018

High-Performance Sparse Matrix-Matrix Products on Intel KNL and Multicore Architectures

Sparse matrix-matrix multiplication (SpGEMM) is a computational primitive that is vastly used in areas ranging from traditional numerical applications to recent big data analysis and machine learning. While many SpGEMM algorithms have been proposed, hardware specific optimizations for multi- and many-core processors are lacking and a detailed analysis of their performance under various use cases and matrices is not available. We firstly identify and mitigate multiple bottlenecks with memory management and thread scheduling on Intel Xeon Phi (Knights Landing or KNL). Specifically targeting multi- and many-core processors, we develop a hash-table-based algorithm and optimize a heap-based shared-memory SpGEMM algorithm. We investigate their performance together with other publicly available codes. Different from the literature, our evaluation also includes use cases that are representative of real graph algorithms, such as multi-source breadth-first search or triangle counting. Our hash-table and heap-based algorithms are showing significant speedups from libraries in the majority of the cases while different algorithms dominate the other scenarios with different matrix size, sparsity, compression factor and operation type. We wrap up in-depth evaluation results and make a recipe to give the best SpGEMM algorithm for target scenario. In conclusion, a critical finding is that hash-table-based SpGEMM gets a significant performance boost if the nonzeros are not required to be sorted within each row of the output matrix.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

OpenMP and MPI implementations of an elasto-viscoplastic fast Fourier transform-based micromechanical solver for fast crystal plasticity modeling

Here, we explore several parallel implementations of an elasto-viscoplastic fast Fourier transform (EVPFFT) model using Message Passing Interface (MPI), OpenMP, and a hybrid of MPI and OpenMP to efficiently predict micromechanical response of polycrystals. Performance studies using EVPFFT are performed based on domain decomposition over voxels of a periodic cell, which is a representative volume element (RVE) of polycrystalline copper. We begin by parallelizing the computationally intensive Newton–Raphson (NR) single crystal solver within EVPFFT. Next, we compare the performance of the serial and parallel FFTW (Fastest Fourier Transform in the West) using OpenMP (OpenMP-FFTW) and MPI (MPI-FFTW) with the original Numerical Recipes-based FOURN routine within EVPFFT. In the parallel environment, we find that the FFT calculations are best performed using the MPI version of FFTW. Finally, the remainder of the code, except read/write subroutines, is parallelized. Significant speedups of the original EVPFFT model are achieved using MPI on shared memory multicore workstations. Furthermore, results achieved on a distributed memory Cray supercomputer show promising strong and weak scalability and in some cases even super scalability for the single crystal NR solver in EVPFFT. MPI-FFTW also scales perfectly for microstructure RVEs larger than 64 3 FFT voxels. For example, the MPI-EVPFFT parallel version of the code accelerates the simulations for approximately two orders of magnitude using 64 cores over the old serial code for an RVE size of 128 3 . Finally, the parallel EVPFFT code developed in this work can run massive voxel-based microstructural RVEs taking the advantages of thousands of logical cores provided by more advanced clusters.

42 ENGINEERING↗

DOE OSTI2020

Machine Learning for Materials Scientists: An Introductory Guide toward Best Practices

This Methods/Protocols article is intended for materials scientists interested in performing machine learning-centered research. Herein, we cover broad guidelines and best practices regarding the obtaining and treatment of data, feature engineering, model training, validation, evaluation and comparison, popular repositories for materials data and benchmarking data sets, model and architecture sharing, and finally publication. In addition, we include interactive Jupyter notebooks with example Python code to demonstrate some of the concepts, workflows, and best practices discussed. Overall, the data-driven methods and machine learning workflows and considerations are presented in a simple way, allowing interested readers to more intelligently guide their machine learning research using the suggested references, best practices, and their own materials domain expertise.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Rb 4 Ag 2 BiBr 9 : A Lead-Free Visible Light Absorbing Halide Semiconductor with Improved Stability

Replacement of the toxic heavy element lead in metal halide perovskites has been attracting a great interest because the high toxicity and poor air stability are two of the major barriers for their widespread utilization. Recently, mixed-cation double perovskite halides, also known as elpasolites, were proposed as an alternative lead-free candidate for the design of nontoxic perovskite solar cells. Herein, we report a new nontoxic and air stable lead-free all-inorganic semiconductor Rb 4 Ag 2 BiBr 9 prepared using the mixed-cation approach; however, Rb 4 Ag 2 BiBr 9 adopts a new structure type (Pearson’s code o P 32) featuring BiBr 6 octahedra and AgBr 5 square pyramids that share common edges and corners to form a unique 2D layered non-perovskite structure. Rb 4 Ag 2 BiBr 9 is also demonstrated to be thermally stable with the measured onset decomposition temperature of T o = 520 °C. Optical absorption measurements and density functional theory calculations suggest a nearly direct band gap for Rb 4 Ag 2 BiBr 9 . Room temperature photoluminescence (PL) measurements show a broadband weak emission. Furthermore, temperature-dependent and power-dependent PL measurements show a strong competition between multiple emission centers and suggest the coexistence of defect-bound excitons and self-trapped excitons in Rb 4 Ag 2 BiBr 9 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Deep phylogenomics of a tandem-repeat galectin regulating appendicular skeletal pattern formation

Background A multiscale network of two galectins Galectin-1 (Gal-1) and Galectin-8 (Gal-8) patterns the avian limb skeleton. Among vertebrates with paired appendages, chondrichthyan fins typically have one or more cartilage plates and many repeating parallel endoskeletal elements, actinopterygian fins have more varied patterns of nodules, bars and plates, while tetrapod limbs exhibit tandem arrays of few, proximodistally increasing numbers of elements. We applied a comparative genomic and protein evolution approach to understand the origin of the galectin patterning network. Having previously observed a phylogenetic constraint on Gal-1 structure across vertebrates, we asked whether evolutionary changes of Gal-8 could have critically contributed to the origin of the tetrapod pattern. Results Translocations, duplications, and losses of Gal-8 genes in Actinopterygii established them in different genomic locations from those that the Sarcopterygii (including the tetrapods) share with chondrichthyans. The sarcopterygian Gal-8 genes acquired a potentially regulatory non-coding motif and underwent purifying selection. The actinopterygian Gal-8 genes, in contrast, did not acquire the non-coding motif and underwent positive selection. Conclusion These observations interpreted through the lens of a reaction-diffusion-adhesion model based on avian experimental findings can account for the distinct endoskeletal patterns of cartilaginous, ray-finned, and lobe-finned fishes, and the stereotypical limb skeletons of tetrapods.

Evolutionary Biology↗

DOE OSTI2019

LArSoft and Future Framework Directions at Fermilab [Slides]

The diversity of the scientific goals across HEP experiments necessitates unique bodies of software tailored for achieving particular physics results. The challenge, however, is to identify the software that must be unique, and the code that is unnecessarily duplicated, which results in wasted effort and inhibits code maintainability. Fermilab has a history of supporting and developing software projects that are shared among HEP experiments. Fermilab's scientific computing division currently expends effort in maintaining and developing the LArSoft toolkit, used by liquid argon TPC experiments, as well as the event-processing framework technologies used by LArSoft, CMS, DUNE, and the majority of Fermilab-hosted experiments. As computing needs for DUNE and the HL-LHC become clearer, the computing models are being rethought. This talk will focus on Fermilab's plans for addressing the evolving software landscape as it relates to LArSoft and the event-processing frameworks, and how commonality among experiment software can be achieved while still supporting customizations necessary for a given experiment's physics goals.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2016

Nucleic acids encoding plant glutamine phenylpyruvate transaminase (GPT) and uses thereof

Glutamine phenylpyruvate transaminase (GPT) proteins, nucleic acid molecules encoding GPT proteins, and uses thereof are disclosed. Provided herein are various GPT proteins and GPT gene coding sequences isolated from a number of plant species. As disclosed herein, GPT proteins share remarkable structural similarity within plant species, and are active in catalyzing the synthesis of 2-hydroxy-5-oxoproline (2-oxoglutaramate), a powerful signal metabolite which regulates the function of a large number of genes involved in the photosynthesis apparatus, carbon fixation and nitrogen metabolism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Nucleic acids encoding plant glutamine phenylpyruvate transaminase (GPT) and uses thereof

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

LArSoft and Future Framework Directions at Fermilab

The diversity of the scientific goals across HEP experiments necessitates unique bodies of software tailored for achieving particular physics results. The challenge, however, is to identify the software that must be unique, and the code that is unnecessarily duplicated, which results in wasted eort and inhibits code maintainability. Fermilab has a history of supporting and developing software projects that are shared among HEP experiments. Fermilab’s scientific computing division currently expends eort in maintaining and developing the LArSoft toolkit, used by liquid argon TPC experiments, as well as the event-processing framework technologies used by LArSoft, CMS, DUNE, and the majority of Fermilab-hosted experiments. As computing needs for DUNE and the HL-LHC become clearer, the computing models are being rethought. Presented here are Fermilab’s plans for addressing the evolving software landscape as it relates to LArSoft and the data-processing frameworks, specifically as it relates to the DUNE experiment.

Knoepfel, Kyle↗

DOE OSTI2024

CG-Kit: Code Generation Toolkit for performant and maintainable variants of source code applied to Flash-X hydrodynamics simulations

CG-Kit is a new Code Generation tool-Kit that we have developed as a part of the solution for portability and maintainability for multiphysics computing applications. The development of CG-Kit is rooted in the urgent need created by the shifting landscape of high-performance computing platforms and the algorithmic complexities of a particular large-scale multiphysics application: Flash-X. To efficiently use computing resources on a heterogeneous node, an application must have a map of computation to resources and a mechanism to move the data and computation to the resources according to the map. Most existing performance portability solutions are focussed on abstracting the expression of computations so that a unified source code can be specialized to run on different resources. However, such an approach is insufficient for a code like Flash-X, which has a multitude of code components that can be assembled in various permutations and combinations to form different instances of applications. Similar challenges apply to any code that has composability, where a single specified way of apportioning work among devices may not be optimal. Additionally, use cases arise where the optimal control flow of computation may differ for different devices while the underlying numerics remain identical. This combination leads to unique challenges including handling an existing large code base in Fortran and/or C/C++, subdivision of code into a great variety of units supporting a wide range of physics and numerical methods, different parallelization techniques for distributed and shared memory systems and accelerator devices, and heterogeneity of computing platforms requiring coexisting variants of parallel algorithms. All of these challenges demand that scientific software developers apply existing knowledge about domain applications, algorithms, and computing platforms to determine custom abstractions and granularity for code generation. There is a critical lack of tools to tackle those problems. CG-Kit is designed to fill this gap by providing a user with the ability to express their desired control flow and computation-to-resource map in the form a pseudocode-like recipe. It consists of standalone tools that can be combined into highly specific and, we argue, highly effective portability and maintainability toolchains. Here we present the design of our new tools: parametrized source trees, control flow graphs, and recipes. The tools are implemented in Python. They are agnostic to the programming language of the source code targeted for code generation. In conclusion, we demonstrate the capabilities of the toolkit with two examples, first, multithreaded variants of the basic AXPY operation, and second, variants of parallel algorithms within a hydrodynamics solver, called Spark, from Flash-X that operates on block-structured adaptive meshes.

Algorithmic portability↗

DOE OSTI2018

Breaking the 20 Year Logjam to Better Insulating Windows

Windows account for about 4 Quads of US energy consumption or 12% of building energy use. After 20 years of public/private investment (from 1980 to 2000) in technology R&D, coupled with new rating and labeling organizations (NFRC) and with subsequent voluntary ENERGY STAR programs and tighter codes and standards, windows using low-E/argon gas fill (~R3) gained a dominant market share and now account for >86% of all annual sales. However, this remarkable transformation of prior markets has stagnated, with triple glazing (~R5-R7) comprising less than 2% of all window sales in 2016. U.S. Department of Energy (DOE) estimates the national technical potential savings using R5-R7 windows at ~ 2Q annually, but manufacturers claim they would have to redesign their entire sash/frame inventory to accommodate the thicker and heavier insulating glass units (IGUs) in conventional triple glazed window designs. This paper outlines the initial findings from the first phase of a new collaborative effort, now underway with industry partners, to transform window markets to the R5-R7 insulating levels by introducing a novel packaging of existing technology elements, implemented as a “drop-in replacement” for current IGUs. The program involves working with supply chain partners to ensure component availability, with leading window manufacturers to integrate the key technology elements and initially deploy the new designs, and with market pull partners to speed adoptions rates. Market pull partners include building code officials, utility rebate/incentive programs and ENERGY STAR, along with early adopters such as builders targeting net zero and passive house designers.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Keras2c: A library for converting Keras neural networks to real-time compatible C

With the growth of machine learning models and neural networks in measurement and control systems comes the need to deploy these models in a way that is compatible with existing systems. Existing options for deploying neural networks either introduce very high latency, require expensive and time consuming work to integrate into existing code bases, or only support a very limited subset of model types. We have therefore developed a new method called Keras2c, which is a simple library for converting Keras/TensorFlow neural network models into real-time compatible C code. It supports a wide range of Keras layers and model types including multidimensional convolutions, recurrent layers, multi-input/output models, and shared layers. Keras2c re-implements the core components of Keras/TensorFlow required for predictive forward passes through neural networks in pure C, relying only on standard library functions considered safe for real-time use. The core functionality consists of ~ 1500 lines of code, making it lightweight and easy to integrate into existing codebases. Keras2c has been successfully tested in experiments and is currently in use on the plasma control system at the DIII-D National Fusion Facility at General Atomics in San Diego.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2015

Gregarious Data Re-structuring in a Many Core Architecture

this paper, we have developed a new methodology that takes in consideration the access patterns from a single parallel actor (e.g. a thread), as well as, the access patterns of “grouped” parallel actors that share a resource (e.g. a distributed Level 3 cache). We start with a hierarchical tile code for our target machine and apply a series of transformations at the tile level to improve data residence in a given memory hierarchy level. The contribution of this paper includes (a) collaborative data restructuring for group reuse and (b) low overhead transformation technique to improve access pattern and bring closely connected data elements together. Preliminary results in a many core architecture, Tilera TileGX, shows promising improvements over optimized OpenMP code (up to 31% increase in GFLOPS) and over our own previous work on fine grained runtimes (up to 16%) for selected kernels

data restructuring↗

DOE OSTI2017

Modern gyrokinetic particle-in-cell simulation of fusion plasmas on top supercomputers

The Gyrokinetic Toroidal Code at Princeton (GTC-P) is a highly scalable and portable particle-in-cell (PIC) code. It solves the 5D Vlasov-Poisson equation featuring efficient utilization of modern parallel computer architectures at the petascale and beyond. Motivated by the goal of developing a modern code capable of dealing with the physics challenge of increasing problem size with sufficient resolution, new thread-level optimizations have been introduced as well as a key additional domain decomposition. GTC-P's multiple levels of parallelism, including inter-node 2D domain decomposition and particle decomposition, as well as intra-node shared memory partition and vectorization have enabled pushing the scalability of the PIC method to extreme computational scales. In this paper, we describe the methods developed to build a highly parallelized PIC code across a broad range of supercomputer designs. This particularly includes implementations on heterogeneous systems using NVIDIA GPU accelerators and Intel Xeon Phi (MIC) co-processors and performance comparisons with state-of-the-art homogeneous HPC systems such as Blue Gene/Q. New discovery science capabilities in the magnetic fusion energy application domain are enabled, including investigations of Ion-Temperature-Gradient (ITG) driven turbulence simulations with unprecedented spatial resolution and long temporal duration. Performance studies with realistic fusion experimental parameters are carried out on multiple supercomputing systems spanning a wide range of cache capacities, cache-sharing configurations, memory bandwidth, interconnects and network topologies. Furthermore, these performance comparisons using a realistic discovery-science-capable domain application code provide valuable insights on optimization techniques across one of the broadest sets of current high-end computing platforms worldwide.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2015

Targeted Switchgrass BAC Library Screening and Sequence Analysis Identifies Predicted Biomass and Stress Response-Related Genes

To identify switchgrass homologs of rice genes, known/predicted to control biomass and stress response-related traits, we screened 96,000 clones from two switchgrass bacterial artificial chromosome (BAC) libraries. Full-length sequencing of 311 BAC clones revealed sequence for ~3.2 % (51.7 Mb) of the switchgrass genome, coding for 3948 genes. A comparison with Arabidopsis and five grass genomes revealed that switchgrass genes share the highest number of homologs with rice (95.5 %) followed by foxtail millet (91.7 %) and Sorghum (91.5 %). One hundred eighteen of the annotated genes are unique to switchgrass. Gene annotation and ontology analysis revealed 695 genes belonging to gene families targeted in the screening. These include 350 kinase, 203 glycosyltransferase (GT), 109 glycoside hydrolase (GH), and 33 ethylene responsive transcription factor (ERF) family genes. Rice homologs of 65 genes, identified here, have demonstrated roles in bioenergy-relevant traits. These include 14 GT2 family genes involved in the synthesis of cellulose and hemicelluloses. Finally, comparative expression analysis in six switchgrass organs revealed a conserved expression pattern for three cellulose synthase (CesA1, CesA2, and CesA9) and five cellulose-synthase-like genes (CslA2, CslA11, CslC1, CslD4, and CslE6). CslF genes that encode mixed linkage glucans are expressed in wider range of tissues in switchgrass compared with rice.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2018

Empirical profiling of cold hydrogen plumes formed from venting of LH 2 storage vessels

Liquid hydrogen (LH 2 ) storage is viewed as a viable approach to assure sufficient hydrogen capacity at commercial fuelling stations. Presently, LH 2 is produced at remote facilities and then transported to the end-use site by road vehicles (i.e., LH 2 tanker trucks). Venting of hydrogen to depressurize the transport storage tank is a routine part of the LH 2 delivery and site transfer process. The behavior of cold hydrogen plumes has not been well characterized because of the sparsity of empirical field data, which can lead to overly conservative safety requirements. Committee members of the National Fire Protection Association (NFPA) Standard 2 [1] formed the Hydrogen Storage Safety Task Group, which consists of hydrogen producers, safety experts, and computational fluid dynamics modellers, has identified the lack of understanding of hydrogen dispersion during LH 2 venting of storage vessels as a critical gap for establishing safety distances at LH 2 facilities, especially commercial hydrogen fuelling stations. To address this need, the National Renewable Energy Laboratory Sensor Laboratory, in collaboration with the NFPA Hydrogen Storage Task Group, developed a prototype Cold Hydrogen Plume Analyzer to empirically characterize the hydrogen plume formed during LH 2 storage tank venting. The prototype analyzer was field deployed during an actual LH 2 venting process. Critical findings included: hydrogen above the lower flammable limit (LFL) was detected as much as 2 m lower than the release point, which is not predicted by existing models; personal monitors detected hydrogen at ground level, although at levels below the LFL; a small but inconsistent correlation was found between oxygen depletion and the hydrogen concentration; a negligible to non-existent correlation was found between in-situ temperature measurements and the hydrogen concentration. The prototype analyzer is being upgraded for enhanced metrological capabilities, including improved real-time spatial and temporal profiling of hydrogen plumes and tracking of prevailing weather conditions. Additional deployments are planned to monitor plume behavior under different wind, humidity, and temperature conditions. The data will be shared with the Hydrogen Storage Task Group and ultimately will be used support theoretical models and code requirements prescribed in NFPA 2.

08 HYDROGEN↗

DOE OSTI2019

Solar soft cost ontology: a review of solar soft costs

The continued deployment of renewable energy is critical to the energy transition necessary for climate change mitigation. Since cost is one of the primary drivers of solar adoption, we must continue to reduce the cost of solar to hasten the transition to clean energy. Currently, solar soft costs account for 52%–70% of the cost of an installed residential solar photovoltaic (PV) system in the U.S. These costs are persistent and an increasing share of the costs, since hardware costs continue to decline. The consensus on a definition of soft costs is 'non-hardware costs', but defining something by what it is not does not tell us what it is, which makes it difficult to study and understand. To this end, we developed the solar soft cost ontology (SSCO) to provide a comprehensive view of the complex and variable knowledge domain of solar soft costs. In this work, we identify the main categories of solar soft costs, how they relate to each other, how that knowledge is created, shared, and acquired, and the relevant actors in the solar soft cost knowledge ecosystem. Developed by coding a corpus of nearly 130 academic articles, the resulting ontology in web ontology language (OWL) includes 136 classes and 87 properties. The SSCO can facilitate and enhance knowledge transfer of research findings and best practices within and between stakeholder communities, including researchers, practitioners, and policy makers, that seek to address solar soft costs. We conclude with a discussion of potential applications and use cases for the SSCO among this diverse stakeholder community.

14 SOLAR ENERGY↗