Results for “library call selection”

DOE OSTI2012

Empirical Performance Model-Driven Data Layout Optimization and Library Call Selection for Tensor Contraction Expressions

Empirical optimizers like ATLAS have been very effective in optimizing computational kernels in libraries. The best choice of parameters such as tile size and degree of loop unrolling is determined by executing different versions of the computation. In contrast, optimizing compilers use a model-driven approach to program transformation. While the model-driven approach of optimizing compilers is generally orders of magnitude faster than ATLAS-like library generators, its effectiveness can be limited by the accuracy of the performance models used. In this paper, we describe an approach where a class of computations is modeled in terms of constituent operations that are empirically measured, thereby allowing modeling of the overall execution time. The performance model with empirically determined cost components is used to perform data layout optimization together with the selection of library calls and layout transformations in the context of the Tensor Contraction Engine, a compiler for a high-level domain-specific language for expressing computational models in quantum chemistry. The effectiveness of the approach is demonstrated through experimental measurements on representative computations from quantum chemistry.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2017

Puromycin-resistant lentiviral control shRNA vector, pLKO.1 induces unexpected cellular differentiation of P19 embryonic stem cells

RNA silencing is used as a common method for investigating loss-of-function effects of genes of interest. In mammalian cells, RNA interference (RNAi) or RNA silencing can be achieved by transient siRNA (small or short interfering RNA) transfection or by stable shRNA (short hairpin RNA) systems. Various vectors are used for efficient delivery of shRNA. Lentiviral vectors offer an efficient delivery system for stable and long-term expression of the shRNA in mammalian cells. The widely used lentiviral pLKO.1 plasmid vector is very popular in RNAi studies. A large RNAi database, a TRC (the RNAi Consortium) library, was established based on the pLKO.1-TRC plasmid vector. This plasmid (also called pLKO.1-puro) has a puromycin-resistant gene for selection in mammalian cells along with designs for generating lentiviral particles as well for RNA silencing. While using the pLKO.1-puro TRC control shRNA plasmid for transfection in murine P19 embryonic stem (ES) cells, it was unexpectedly discovered that this plasmid vector induced robust endodermal differentiation. Since P19 ES cells are pluripotent and respond to external stimuli that have the potential to alter the phenotype and thus its stemness, other cell types used in RNA silencing studies do not display the obvious effect and therefore, may affect experiments in subtle ways that would go undetected. This study for the first time provides evidence that raises concern and warrants extreme caution while using the pLKO.1-puro control shRNA vector because of its unexpected non-specific effects on cellular integrity. - Highlights: • In P19 ES cells the pLKO.1-puro lentiviral control shRNA vector induced endodermal differentiation. • P19 ES cells harboring the pCDNA3 plasmid vector retained their stem-ness as opposed to those harboring the pLKO.1-puro vector. • P19 ES cells can serve as a sensor to determine vector safety. • Extreme caution is warranted while using the widely used pLKO.1-puro lentiviral vector for experimental and therapeutic designs.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2013

SDS: A Framework for Scientific Data Services

Large-scale scientific applications typically write their data to parallel file systems with organizations designed to achieve fast write speeds. Analysis tasks frequently read the data in a pattern that is different from the write pattern, and therefore experience poor I/O performance. In this paper, we introduce a prototype framework for bridging the performance gap between write and read stages of data access from parallel file systems. We call this framework Scientific Data Services, or SDS for short. This initial implementation of SDS focuses on reorganizing previously written files into data layouts that benefit read patterns, and transparently directs read calls to the reorganized data. SDS follows a client-server architecture. The SDS Server manages partial or full replicas of reorganized datasets and serves SDS Clients' requests for data. The current version of the SDS client library supports HDF5 programming interface for reading data. The client library intercepts HDF5 calls and transparently redirects them to the reorganized data. The SDS client library also provides a querying interface for reading part of the data based on user-specified selective criteria. We describe the design and implementation of the SDS client-server architecture, and evaluate the response time of the SDS Server and the performance benefits of SDS.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Cas3-Mediated Genome Reduction: Demonstration in Cupriavidus Necator H16 Improves Growth on Heterotrophic and Autotrophic Carbon Sources

Genome reduction is widely used to improve microbial bioprocessing hosts by reducing the burden of inessential physiology. Rationally identifying genomic regions that are dispensable or even detrimental to bioprocessing is challenged by our inability to map genome sequence to function across complex regulation and physiology. Thus, there is a need for tools that rapidly generate reduced genome strains with improved performance in process-relevant conditions. Here, we report a Cascade-Cas3-enabled method called TRIM3 that generates large deletions by targeting a randomly integrated transposon, enabling facile generation of a genome-reduced mutant library. Mutants with improved performance were isolated following growth-coupled selection and analyzed by long-read DNA sequencing to identify deletions in their genomes. We deploy this system iteratively in the industrial host Cupriavidus necator H16 on fructose and on formate. After two rounds of TRIM3, we isolate a strain containing a total reduction of 1.4 Mb (18.4% of the genome) that grows 25% faster in a bioreactor on fructose and a strain with a total reduction of 0.5 Mb (7.3% of the genome) that grows 14% faster on formate. This work demonstrates a method for random, iterative, growth-selectable genome reduction that represents a new avenue for large-scale genome modifications and the development of improved bioprocessing hosts.

09 BIOMASS FUELS↗

DOE OSTI2017

An ALMA Survey of Submillimeter Galaxies in the Extended Chandra Deep Field South: Spectroscopic Redshifts

We present spectroscopic redshifts of S{sub 870μm} ≳2 mJy submillimeter galaxies (SMGs), which have been identified from the ALMA follow-up observations of 870 μm detected sources in the Extended Chandra Deep Field South (the ALMA-LESS survey). We derive spectroscopic redshifts for 52 SMGs, with a median of z = 2.4 ± 0.1. However, the distribution features a high-redshift tail, with ∼23% of the SMGs at z⩾3. Spectral diagnostics suggest that the SMGs are young starbursts, and the velocity offsets between the nebular emission and UV ISM absorption lines suggest that many are driving winds, with velocity offsets of up to 2000 km s{sup −1}. Using the spectroscopic redshifts and the extensive UV-to-radio photometry in this field, we produce optimized spectral energy distributions (SEDs) using Magphys, and use the SEDs to infer a median stellar mass of M{sub ⋆} = (6 ± 1)× 10{sup 10} M {sub ⊙} for our SMGs with spectroscopic redshift. By combining these stellar masses with the star formation rates (measured from the far-infrared SEDs), we show that SMGs (on average) lie a factor of ∼5 above the so-called “main sequence” at z∼2. We provide this library of 52 template fits with robust and uniquely well-sampled SEDs as a resource for future studies of SMGs, and also release the spectroscopic catalog of ∼2000 (mostly infrared-selected) galaxies targeted as part of the spectroscopic campaign.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

HPC Campaign Management: Remote data access with user-defined error bound using ADIOS and ZFP

Remote access to large-scale scientific datasets, like those generated by combustion simulations or other high-performance computing (HPC) applications, presents a significant challenge. Downloading entire datasets is often impractical due to their size and the bandwidth limitations of typical networks. To address this challenge, we propose a novel approach that enables efficient remote access to large datasets distributed across multiple facilities. Our method enables technologies to download only the data values of a select variable, in a select region of interest, to a user-defined accuracy. For this purpose, we extended the ADIOS IO library to provide read functions with user-defined accuracy, a remote data server that understands multidimensional selections of specific variables, steps and accuracy from an ADIOS dataset, and which uses lossy compression on the remote site to reduce the data to be transferred back to the client. In addition, our extension of the ADIOS library collects metadata from multiple datasets in small files called Campaign Archives, which can be shared among project participants on any HPC, cloud or laptop, and which can easily facilitate the discovery of content and pointers to the data location as well as remote access to the data by local tools as if data was local. This feature called Campaign Management, enables a group of scientists to manage related datasets stored in multiple files, across multiple facilities as if it was in a single file/database. We demonstrate the effectiveness of our approach using a 1.5 TB dataset from the S3D combustion simulation on Frontier at the Oak Ridge Leadership Facility. Even a single variable from this dataset, at 64 GB, is too large to be processed on a standard laptop. We show two different reading patterns for 2D plots and 3D visualization, with careful settings that a scientist studying combustion data would do and show that running the same Python scripts on Frontier directly takes comparable time than running them on the local laptop with remote access to the data on Frontier.

Podhorszki, Norbert [ORNL] (ORCID:000000019647542X↗

DOE OSTI2022

Next generation experimental data access at NSLS-II

The NSLS-II network and computing infrastructure has been significantly updated recently. The re-IP process in 2020-2021 enabled the NSLS-II network to be routable to the rest of the BNL campus. Then, standardization of the operating systems and deployment procedures helped to deliver a consistent environment to workstations and servers used by all NSLS-II beamlines. In particular, the RedHat Enterprise Linux 8 was deployed to 700+ machines using the RedHat Satellite infrastructure management product, and all critical services (IOCs, databases, etc.) were migrated to the new OS. NFS users’ home directories are consistent across all of the machines, which eliminates the need for the individual configuration of the user environment on each host. The standard suite of software packages is available to the beamline staff and users, which includes the system packages (deployed via RPM) as well as the conda environments for data acquisition and analysis. Security measures were implemented to comply with the industry standards, which include multi-factor authentication (using Duo), secure screen lock for the beamline machines, and advanced access control to the experimental data that is stored in shared central storage available on all hosts. These major enhancements facilitated sharing the experimental data (currently for a number of selected beamlines, with a plan to extend it to the whole facility in the nearest future) with the users via an externally facing JupyterHub instance. The beamlines keep using the Bluesky data acquisition framework to orchestrate their experiments, and the new infrastructure enabled them to use a next-generation data access library called tiled.

36 MATERIALS SCIENCE↗

DOE OSTI2012

Evaluating and adjusting {sup 239}Pu, {sup 56}Fe, {sup 28}Si and {sup 95}Mo nuclear data with a Monte Carlo technique

In this paper, Monte Carlo optimization and nuclear data evaluation are combined to produce optimal adjusted nuclear data files. The methodology is based on the so-called 'Total Monte Carlo' and the TALYS system. Not only a single nuclear data file is produced for a given isotope, but virtually an infinite number, defining probability distributions for each nuclear quantity. Then each of these random nuclear data libraries is used in a series of benchmark calculations. With a goodness-of-fit estimator, best {sup 239}Pu, {sup 56}Fe, {sup 28}Si and {sup 95}Mo evaluations for that benchmark set can be selected. A few thousands of random files are used and each of them is tested with a large number of fast, thermal and intermediate energy criticality benchmarks. From this, the best performing random file is chosen and proposed as the optimum choice among the studied random set. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2024

KBase Narrative - Porphyromonadaceae sp. W3.11 genome

Narratives for The phenotype and genotype of fermentative prokaryotes This is the Narrative for Porphyromonadaceae sp. W3.11. A complementary Narrative for Lachnospiraceae sp. C1.1 is available here. This is the Narrative for Lachnospiraceae sp. C1.1. A complementary Narrative for Porphyromonadaceae sp. W3.11 is available here. Background and Isolation This Narrative and its complementary Narrative contain assembly and annotation of two bacterial isolates that were isolated by our laboratory from the rumen of a Holstein heifer. All procedures with animals have been approved by University of California Davis’s Institutional Animal Care and Use Committee. Rumen contents were collected through a rumen fistula and strained through two layers of cheesecloth into a bottle. The bottle was sealed to exclude air and maintained at 39°C. Contents were brought to the laboratory and bubbled under O2-free CO2 within 15 min. At the laboratory, serial dilutions were made with anaerobic dilution solution for Lachnospiraceae sp. C1.1 and propionibacterium diluent for Porphyromonadaceae sp. W3.11 (table S2). Aliquots (0.1 ml) of each dilution were injected into anaerobic bottle plates (1) containing 9 ml of LH medium (table S2). After incubation at 37°C for 7 days, isolated colonies were picked. Lachnospiraceae sp. C1.1 was picked from a bottle inoculated with a 104 dilution of rumen contents, and Porphyromonadaceae sp. W3.11 was picked from a bottle inoculated with a 103 dilution. After initial isolation, these organisms were purified by growing on anaerobic roll tubes (2) and picking isolated colonies. We performed de novo sequencing of Lachnospiraceae sp. C1.1 and Porphyromonadaceae sp. W3.11. Aliquots of liquid culture (9 and 1.5 ml, respectively) were collected by syringe and centrifuged (21,000g for 10 min at 4°C). Cell pellets were submitted to Molecular Research LP for DNA extraction, library preparation, and sequencing. After resuspending pellets in 180 µl of ATL buffer (Qiagen), DNA was extracted using the MagAttract HMW DNA Kit (Qiagen). DNA was eluted in 100 µl of AE buffer (Qiagen) and then cleaned using the DNEasy PowerClean Pro Cleanup Kit (Qiagen). DNA was then sheared using the Covaris g-TUBE (Covaris). Sequencing libraries were prepared using the SMRTbell Express Template Prep Kit 2.0 (Pacific Biosciences) and 1500 ng of the sheared and purified DNA. The SMRTbell libraries were size-selected (>6 Kb) using a BluePippin instrument (Sage Science) and 0.75% agarose gel. Libraries were then sequenced using the PacBio Sequel II (Pacific Biosciences) platform and a 30-hour movie time. Narrative Summary In these Narratives, we filtered low-quality reads using Trimmomatic (v0.36), assembled filtered reads with SPAdes (v3.15.3), and then checked completeness and contamination of the assembled genomes with CheckM (v1.0.18). Statistics for sequencing and assembly are in table S3. Using the assembled contigs (genomes), we called genes and annotated them. Protein-coding genes were called using Prodigal (v2.6.3) (3) locally or using KBase via RASTtk (v1.073), with identical results. Genes were annotated with KO IDs using KAAS (4). They were further annotated with pfam and TIGRFAM IDs using KBase and the Annotate Domains in a Genome app. We classified putative genes for hydrogenases using HydDB. Genes for 16S ribosomal RNA (rRNA) were called using RASTtk (v1.073) in KBase. The contigs (genomes) were analyzed to determine whether they belonged to new species. Taxonomy was assigned using GTDB-Tk (v1.7.0) in KBase. The identity of 16S rRNA genes to other organisms was found using EzBioCloud (5). Values of digital DNA-DNA hybridization (dDDH) were found with Type (Strain) Genome Server (6). These analyses suggest that Lachnospiraceae sp. C1.1 and Porphyromonadaceae sp. W3.11 represent novel species or genera. GTDB-Tk assigned Lachnospiracae sp. C1.1 to family Lachnospiraceae and genus NK4A144, which contains no type strains. It assigned Porphyromonadaceae sp. W3.11 to Porphyromonadaceae and genus Porphyromonas_A. Values of 16S rRNA identity and dDDH with respect to type strains were low (table S4). Although more phenotypic data are needed, available evidence supports assignment of genomes to new species or genera. Related publication Hackmann TJ, Zhang B. The phenotype and genotype of fermentative prokaryotes. Sci Adv. 2023 Sep 29;9(39):eadg8687. doi: 10.1126/sciadv.adg8687. Epub 2023 Sep 27. PMID: 37756392; PMCID: PMC10530074.

Hackmann, Timothy↗

DOE OSTI2024

KBase Narrative - Lachnospiraceae sp. C1.1 genome

Narratives for The phenotype and genotype of fermentative prokaryotes This is the Narrative for Porphyromonadaceae sp. W3.11. A complementary Narrative for Lachnospiraceae sp. C1.1 is available here. This is the Narrative for Lachnospiraceae sp. C1.1. A complementary Narrative for Porphyromonadaceae sp. W3.11 is available here. Background and Isolation This Narrative and its complementary Narrative contain assembly and annotation of two bacterial isolates that were isolated by our laboratory from the rumen of a Holstein heifer. All procedures with animals have been approved by University of California Davis’s Institutional Animal Care and Use Committee. Rumen contents were collected through a rumen fistula and strained through two layers of cheesecloth into a bottle. The bottle was sealed to exclude air and maintained at 39°C. Contents were brought to the laboratory and bubbled under O2-free CO2 within 15 min. At the laboratory, serial dilutions were made with anaerobic dilution solution for Lachnospiraceae sp. C1.1 and propionibacterium diluent for Porphyromonadaceae sp. W3.11 (table S2). Aliquots (0.1 ml) of each dilution were injected into anaerobic bottle plates (1) containing 9 ml of LH medium (table S2). After incubation at 37°C for 7 days, isolated colonies were picked. Lachnospiraceae sp. C1.1 was picked from a bottle inoculated with a 104 dilution of rumen contents, and Porphyromonadaceae sp. W3.11 was picked from a bottle inoculated with a 103 dilution. After initial isolation, these organisms were purified by growing on anaerobic roll tubes (2) and picking isolated colonies. We performed de novo sequencing of Lachnospiraceae sp. C1.1 and Porphyromonadaceae sp. W3.11. Aliquots of liquid culture (9 and 1.5 ml, respectively) were collected by syringe and centrifuged (21,000g for 10 min at 4°C). Cell pellets were submitted to Molecular Research LP for DNA extraction, library preparation, and sequencing. After resuspending pellets in 180 µl of ATL buffer (Qiagen), DNA was extracted using the MagAttract HMW DNA Kit (Qiagen). DNA was eluted in 100 µl of AE buffer (Qiagen) and then cleaned using the DNEasy PowerClean Pro Cleanup Kit (Qiagen). DNA was then sheared using the Covaris g-TUBE (Covaris). Sequencing libraries were prepared using the SMRTbell Express Template Prep Kit 2.0 (Pacific Biosciences) and 1500 ng of the sheared and purified DNA. The SMRTbell libraries were size-selected (>6 Kb) using a BluePippin instrument (Sage Science) and 0.75% agarose gel. Libraries were then sequenced using the PacBio Sequel II (Pacific Biosciences) platform and a 30-hour movie time. Narrative Summary In these Narratives, we filtered low-quality reads using Trimmomatic (v0.36), assembled filtered reads with SPAdes (v3.15.3), and then checked completeness and contamination of the assembled genomes with CheckM (v1.0.18). Statistics for sequencing and assembly are in table S3. Using the assembled contigs (genomes), we called genes and annotated them. Protein-coding genes were called using Prodigal (v2.6.3) (3) locally or using KBase via RASTtk (v1.073), with identical results. Genes were annotated with KO IDs using KAAS (4). They were further annotated with pfam and TIGRFAM IDs using KBase and the Annotate Domains in a Genome app. We classified putative genes for hydrogenases using HydDB. Genes for 16S ribosomal RNA (rRNA) were called using RASTtk (v1.073) in KBase. The contigs (genomes) were analyzed to determine whether they belonged to new species. Taxonomy was assigned using GTDB-Tk (v1.7.0) in KBase. The identity of 16S rRNA genes to other organisms was found using EzBioCloud (5). Values of digital DNA-DNA hybridization (dDDH) were found with Type (Strain) Genome Server (6). These analyses suggest that Lachnospiraceae sp. C1.1 and Porphyromonadaceae sp. W3.11 represent novel species or genera. GTDB-Tk assigned Lachnospiracae sp. C1.1 to family Lachnospiraceae and genus NK4A144, which contains no type strains. It assigned Porphyromonadaceae sp. W3.11 to Porphyromonadaceae and genus Porphyromonas_A. Values of 16S rRNA identity and dDDH with respect to type strains were low (table S4). Although more phenotypic data are needed, available evidence supports assignment of genomes to new species or genera. Related publication Hackmann TJ, Zhang B. The phenotype and genotype of fermentative prokaryotes. Sci Adv. 2023 Sep 29;9(39):eadg8687. doi: 10.1126/sciadv.adg8687. Epub 2023 Sep 27. PMID: 37756392; PMCID: PMC10530074.

Hackmann, Timothy↗

DOE OSTI2014

Parallel processing of filtered queries in attributed semantic graphs

Execution of complex analytic queries on massive semantic graphs is a challenging problem in big-data analytics that requires high-performance parallel computing. In a semantic graph, vertices and edges carry attributes of various types and the analytic queries typically depend on the values of these attributes. Thus, the computation must view the graph through a filter that passes only those individual vertices and edges of interest. Previous investigations have developed Knowledge Discovery Toolbox (KDT), a sophisticated Python library for parallel graph computations. In KDT, the user can write custom graph algorithms by specifying operations between edges and vertices ( semiring operations ). The user can also customize existing graph algorithms by writing filters. While the high-level language for this customization enables domain scientists to productively express their graph analytics requirements, the customized queries perform poorly due to the overhead of having to call into the Python virtual machine for each vertex and edge. In this work, we use the Selective Embedded Just-In-Time Specialization (SEJITS) approach to automatically translate semiring operations and filters defined by programmers into a lower-level efficiency language, bypassing the upcall into Python. We evaluate our approach by comparing it with the high-performance Combinatorial BLAS engine and show that our approach combines the benefits of programming in a high-level language with executing in a low-level parallel environment. We increase the system's flexibility by developing techniques that provide users with the ability to define new vertex and edge types from Python. We also present a new Roofline model for graph traversals and show that we achieve performance that is significantly closer to the bounds suggested by the Roofline. Finally, to further understand the complex interaction with the underlying architecture, we present an analysis using performance counters that quantifies the improvement in hardware behavior in the context our SEJITS methodology. Comprehensively, we demonstrate the first known solution to the problem of obtaining high performance from a productivity language when applying graph algorithms selectively on semantic graphs with hundreds of millions of edges and scaling to thousands of processors for graphs.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

On the construction of a new stellar classification template library for the LAMOST spectral analysis pipeline

The LAMOST spectral analysis pipeline, called the 1D pipeline, aims to classify and measure the spectra observed in the LAMOST survey. Through this pipeline, the observed stellar spectra are classified into different subclasses by matching with template spectra. Consequently, the performance of the stellar classification greatly depends on the quality of the template spectra. In this paper, we construct a new LAMOST stellar spectral classification template library, which is supposed to improve the precision and credibility of the present LAMOST stellar classification. About one million spectra are selected from LAMOST Data Release One to construct the new stellar templates, and they are gathered in 233 groups by two criteria: (1) pseudo g – r colors obtained by convolving the LAMOST spectra with the Sloan Digital Sky Survey ugriz filter response curve, and (2) the stellar subclass given by the LAMOST pipeline. In each group, the template spectra are constructed using three steps. (1) Outliers are excluded using the Local Outlier Probabilities algorithm, and then the principal component analysis method is applied to the remaining spectra of each group. About 5% of the one million spectra are ruled out as outliers. (2) All remaining spectra are reconstructed using the first principal components of each group. (3) The weighted average spectrum is used as the template spectrum in each group. Using the previous 3 steps, we initially obtain 216 stellar template spectra. We visually inspect all template spectra, and 29 spectra are abandoned due to low spectral quality. Furthermore, the MK classification for the remaining 187 template spectra is manually determined by comparing with 3 template libraries. Meanwhile, 10 template spectra whose subclass is difficult to determine are abandoned. Finally, we obtain a new template library containing 183 LAMOST template spectra with 61 different MK classes by combining it with the current library.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2019

Optimization of Adjustable Drivetrain Assistance Mechanisms for Efficient Robotic Bipeds

Legged robots promise radical mobility for challenging environments, but must be made more energy efficient to be practical. Historically, legged robot design has required efficiency to be traded against versatility. Much energy is lost in actuators and transmissions because few actuation systems are capable of operating efficiently across the wide range of operating conditions (e.g. different joint speeds and torques) required for legged locomotion. We describe a drivetrain topology that overcomes many of these limitations. Our approach combines high-torque electromagnetic motors and low-loss transmissions with a tailored and adjustable set of joint-specific passive mechanisms called support elements, which modulate the energy flow between motors and joints to minimize the electrical energy consumed. We present an optimization-based design method that draws on available bipedal gait data to select optimal support element configurations and parameters. Simple adjustments may be made to support elements at certain joints to enable a wide variety of locomotion with high efficiency. We present results, specific to the 3D humanoid bipedal STEPPR robot, in which support elements are co-optimized across a library of several gaits, converging on a set of designs that predict an average reduction of electrical energy of more than 50% across a set of 15 gaits, with energy savings reaching as much as 85% for some gaits. Concepts were prototyped and tested on a bench testbed, validating the predicted energy savings. Support elements were implemented on STEPPR, and energy savings of more than 35% were demonstrated.

42 ENGINEERING↗

DOE OSTI2022

Dynamic Decarbonization through Autonomous Physics-Centric Deep Learning and Optimization of Building Operations (Abstract only)

This project directly addresses the primary goal of Area of Interest 2 in the CRADA call: to advance optimization-based integrated energy management systems in commercial and residential buildings. Pacific Northwest National Laboratory (PNNL) and its industry partner PassiveLogic aim to accomplish this by reaching three key objectives. First, to ensure a broad impact in the building controls industry, PNNL will extend its open-source library for predictive control synthesis by augmenting its capabilities with data-driven self-learning of building models and auto-calibration of predictive controllers. The effort will focus on building use cases selected in collaboration with PassiveLogic. The team will specifically address the development of methods for data-driven adaptation of building models, investigation of model architectures that best address specific building types, and automated synthesis of differentiable predictive controllers that optimize diverse objectives. Second, PNNL will collaborate with PassiveLogic to integrate the aforementioned methods with PasiveLogic’s advanced controls platform. The collaborative integration effort will inform the developments under the first objective by providing specific data on the attainable performance of model learning on resource-constrained edge computing platforms. This software integration effort will increase the technical maturity of the developed libraries by exploring the use of software integration tools and methods. Third, PNNL and PassiveLogic will work to improve the technology readiness of the developed predictive controllers by testing their performance in relevant test environments, such as high-fidelity simulation, hardware in the loop, and actual test buildings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2014

Performance Impact of I/O on QMCPack Simulations at the Petascale and Beyond

Traditional petascale applications, such as QMCPack, can scale their computations to completely utilize modern supercomputers like Titan, but they cannot scale their I/O. To preserve scalability, scientists cannot save data at the granularity needed to enable scientific discovery and are forced to use large intervals between two checkpoint calls. In this paper, we work to increase the granularity of the I/O in QMCPack simulations without increasing the I/O associated overhead or compromising the scalability of the simulations. Our solution redesigns the I/O algorithms used by QMCPack to gather finer-grained data at high frequencies and integrate the ADIOS API to select effective I/O methods without major code changes. The extension of a tool such as Skel to mimic the variable I/O in QMCPack allows us to predict the I/O performance of the code when using ADIOS methods at the petascale. We show how I/O libraries like ADIOS allow us to increase the amount of scientific data extracted from QMCPack simulations at the granularity desired by the scientists while keeping the I/O overhead below 10%. We also show how the impact of checkpoint I/O for the QMCPack code using ADIOS is below 5% when using preventive tactics for checkpointing at the petascale and beyond.

Herbein, Stephen N↗

DOE OSTI2017

Final Report: Quantification of Uncertainty in Extreme Scale Computations (QUEST)

QUEST (\url{www.quest-scidac.org}) is a SciDAC Institute that is focused on uncertainty quantification (UQ) in large-scale scientific computations. Our goals are to (1) advance the state of the art in UQ mathematics, algorithms, and software; and (2) provide modeling, algorithmic, and general UQ expertise, together with software tools, to other SciDAC projects, thereby enabling and guiding a broad range of UQ activities in their respective contexts. QUEST is a collaboration among six institutions (Sandia National Laboratories, Los Alamos National Laboratory, the University of Southern California, Massachusetts Institute of Technology, the University of Texas at Austin, and Duke University) with a history of joint UQ research. Our vision encompasses all aspects of UQ in leadership-class computing. This includes the well-founded setup of UQ problems; characterization of the input space given available data/information; local and global sensitivity analysis; adaptive dimensionality and order reduction; forward and inverse propagation of uncertainty; handling of application code failures, missing data, and hardware/software fault tolerance; and model inadequacy, comparison, validation, selection, and averaging. The nature of the UQ problem requires the seamless combination of data, models, and information across this landscape in a manner that provides a self-consistent quantification of requisite uncertainties in predictions from computational models. Accordingly, our UQ methods and tools span an interdisciplinary space across applied math, information theory, and statistics. The MIT QUEST effort centers on statistical inference and methods for surrogate or reduced-order modeling. MIT personnel have been responsible for the development of adaptive sampling methods, methods for approximating computationally intensive models, and software for both forward uncertainty propagation and statistical inverse problems. A key software product of the MIT QUEST effort is the MIT Uncertainty Quantification library, called MUQ (\url{muq.mit.edu}).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

Large-Scale System Monitoring Experiences and Recommendations.

Abstract not provided.

Ahlgren, V.↗

DOE OSTI2015

MO-DE-BRA-02: From Teaching to Learning: Systems-Based-Practice and Practice-Based-Learning Innovations in Medical Physics Education Programs

Purpose: The increasing complexity in the field of radiation medicine and concomitant rise in patient safety concerns call for enhanced systems-level training for future medical physicists and thus commensurate innovations in existing educational program curricula. In this work we report on the introduction of three learning opportunities to augment medical physics educational programs towards building systems-based practice and practice-based learning competencies. Methods: All initiatives were introduced for senior -level graduate students and physics residents in an institution with a newly established medical-physics graduate program and therapeutic-physics residency program. The first, centered on incident learning, was based on a spreadsheet tool that incorporated the reporting structure of the Radiation Oncology-incident Learning System (ROILS), included 120 narratives of published incidents and enabled inter-rater variability calculations. The second, centered on best-practices, was a zero-credit seminar course, where students summarized select presentations from the AAPM virtual library on a weekly basis and moderated class discussions using a point/counterpoint approach. Presentation styles were critiqued. The third; centered on learning-by-teaching, required physics residents to regularly explain fundamental concepts in radiological physics from standard textbooks to board certified physics faculty members. Results: Use of the incident-learning system spreadsheet provided a platform to recast known accidents into the framework of ROILS, thereby increasing awareness of factors contributing to unsafe practice and appreciation for inter-rater variability. The seminar course enhanced awareness of best practices, the effectiveness of presentation styles and encouraged critical thinking. The learn-by-teaching rotation allowed residents to stay abreast of and deepen their knowledge of relevant subjects. Conclusion: The incorporation of systems-driven initiatives broadens comprehension of the wider systems context of medical physics, enhances awareness of resources for innovation, communication and sustained learning while maintaining a metric-driven focus on patient safety within the formative phase of student careers. The initiatives were well-received, feasible, and utilized available or shared-resources translatable across educational programs.

60 APPLIED LIFE SCIENCES↗