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At least 289 records · Page 16

Automated System-wide Event Detection and Classification Using Machine Learning on Synchrophasor Data

As the number of phasor measurement units (PMUs) deployed in a power system increases, and their data volume streamed to the control canter intensifies, operators are facing challenges related to the analysis of such data, which need to be observed and responded to as the measurements are displayed in the Control Room. Humans are generally unable to process such large amount of data efficiently and rapidly. There is an apparent need for automated ways to analyze the data, extract actionable information about occurrence of specific events, and characterize the events quickly and cost effectively. This paper discusses the use of machine learning (ML) to facilitate such tasks by providing automated, highly computationally efficient, and cost-effective ways of extracting actionable information from synchrophasor big data in real-time. We developed Big Data Smart (BDSmart) ML-based prototype tool for the Control Room use that automatically analyses data properties from synchrophasor system measurements taken across the three grid Interconnections in the USA (Western, Eastern and ERCOT). The data collected from several hundreds of PMUs located across the Interconnections over a period of two years have been made available for our extensive study. As a result, we were able to identify a number of big data properties that influence how ML methodology is applied to select, develop, train and test the data models that can eventually be used for the tool implementation. The resulting set of candidate algorithms spans unsupervised, supervised, semi-supervised and transfer-learning approaches. Many ML techniques, such as decision trees, multinomial logistic regression, feed-forward neural networks, K-nearest neighbor, multiclass support vector machine, and single and multi-channel convolutional neural networks, are implemented, and their performance is examined. We offer the results from testing the data models. The novelty of our study is in the approaches for bad data detection and mitigation, selection of a simplified feature for event detection, and data label improvements. As a result, we came up with a list of recommendations for the utilities on how to improve the PMU recording practices to cater to the future ML applications aimed at automating the analysis of synchrophasor data.

Synchrophasors, Machine Learning, System-wide Even↗

Vanderbilt CMS Heavy-Ion Tier-2 Facility (Final Report)

The CMS experiment at the LHC at CERN in Geneva, Switzerland spends a portion of its running time colliding heavy ions. These heavy ion collisions are a key probe of a state of matter known as a Quark Gluon Plasma, which is a state of matter found in the very early universe 10s of microseconds after the big bang. These collision data are recorded by the CMS experiment and then undergo several stages of refinement to extract physics properties of the collisions from the raw data recorded by the various detector subsystems in CMS. In addition, copies of these datasets are stored on tape archival storage for data preservation. This award funds the deployment, maintenance, and operation of a computing facility at Vanderbilt University which processes, stores, and analyzes these collision data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Introduction to the special issue on smart transportation

Transportation is getting smarter and smarter, with the prominence of connected automated vehicle technologies in the global auto industry’s near-term growth strategies, of big data analytics and unprecedented access to sensing data of mobility, and of integration of this analytics into the optimization of mobility and transport. Further, these developments are setting off a wave of smart transportation innovations, which are featured by new methods and applications driven by various forms of sensor data such as GPS, CAN bus, LIDA, images, etc. At the same time, complexities surrounding the use, conflation, and processing of disparate data in near real-time is of essence for the design and development of futuristic smart transportation.

33 ADVANCED PROPULSION SYSTEMS↗

A Learning Framework for Control-Oriented Modeling of Buildings

Buildings consume a significant amount of energy worldwide. Several building optimization and control use cases require models of energy consumption which are control oriented, have high predictive capability, imposes minimal data pre-processing requirements, and have the ability to be adapted continuously to account for changing conditions as new data becomes available. Data driven modeling techniques, that have been investigated so far, while promising in the context of buildings, have been unable to simultaneously satisfy all the requirements mentioned above. In this context, deep learning techniques such as Recurrent Neural Networks (RNNs) hold promise, empowered by advanced computational capabilities and big data opportunities. In this paper, we propose a deep learning based methodology for the development of control oriented models for building energy management and test in on data from a real building. Results show that the proposed methodology outperforms other data driven modeling techniques significantly. We perform a detailed analysis of the proposed methodology along dimensions such as topology, sensitivity, and downsampling. Lastly, we conclude by envisioning a building analytics suite empowered by the proposed deep framework, that can drive several use cases related to building energy management.

buildings↗

Exascale computing and big data

Scientific discovery and engineering innovation requires unifying traditionally separated high-performance computing and big data analytics. The tools and cultures of high-performance computing and big data analytics have diverged, to the detriment of both; unification is essential to address a spectrum of major research domains. The challenges of scale tax our ability to transmit data, compute complicated functions on that data, or store a substantial part of it; new approaches are required to meet these challenges. Finally, the international nature of science demands further development of advanced computer architectures and global standards for processing data, even as international competition complicates the openness of the scientific process.

97 MATHEMATICS AND COMPUTING↗

Query Optimization for Graph Analytics on Linked Data Using SPARQL

Triplestores that support query languages such as SPARQL are emerging as the preferred and scalable solution to represent data and meta-data as massive heterogeneous graphs using Semantic Web standards. With increasing adoption, the desire to conduct graph-theoretic mining and exploratory analysis has also increased. Addressing that desire, this paper presents a solution that is the marriage of Graph Theory and the Semantic Web. We present software that can analyze Linked Data using graph operations such as counting triangles, finding eccentricity, testing connectedness, and computing PageRank directly on triple stores via the SPARQL interface. We describe the process of optimizing performance of the SPARQL-based implementation of such popular graph algorithms by reducing the space-overhead, simplifying iterative complexity and removing redundant computations by understanding query plans. Our optimized approach shows significant performance gains on triplestores hosted on stand-alone workstations as well as hardware-optimized scalable supercomputers such as the Cray XMT.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

Materials informatics: From the atomic-level to the continuum

We report that in recent years materials informatics, which is the application of data science to problems in materials science and engineering, has emerged as a powerful tool for materials discovery and design. This relatively new field is already having a significant impact on the interpretation of data for a variety of materials systems, including those used in thermoelectrics, ferroelectrics, battery anodes and cathodes, hydrogen storage materials, polymer dielectrics, etc. Its practitioners employ the methods of multivariate statistics and machine learning in conjunction with standard computational tools (e.g., density-functional theory) to, for example, visualize and dimensionally reduce large data sets, identify patterns in hyperspectral data, parse microstructural images of polycrystals, characterize vortex structures in ferroelectrics, design batteries and, in general, establish correlations to extract important physics and infer structure-property-processing relationships. In this Overview, we critically examine the role of informatics in several important materials subfields, highlighting significant contributions to date and identifying known shortcomings. We specifically focus attention on the difference between the correlative approach of classical data science and the causative approach of physical sciences. From this perspective, we also outline some potential opportunities and challenges for informatics in the materials realm in this era of big data.

36 MATERIALS SCIENCE↗

SLoG: Large-Scale Logging Middleware for HPC and Big Data Convergence

Cloud developers traditionally rely on purpose-specific services to provide the storage model they need for an application. In contrast, HPC developers have a much more limited choice, typically restricted to a centralized parallel file system for persistent storage. Unfortunately, these systems often offer low performance when subject to highly concurrent, conflicting I/O patterns. This makes difficult the implementation of inherently concurrent data structures such as distributed shared logs. Yet, this data structure is key to applications such as computational steering, data collection from physical sensor grids, or discrete event generators. In this paper we tackle this issue. We present SLoG, shared log middleware providing a shared log abstraction over a parallel file system, designed to circumvent the aforementioned limitations. We evaluate SLoG’s design on up to 100,000 cores of the Theta supercomputer: the results show high append velocity at scale while also providing substantial benefits for other persistent backend storage systems.

big data↗

Engineering Ralstonia eutropha for Production of Isobutanol (IBT) Motor Fuel from Carbon Dioxide, Hydrogen, and Oxygen Project Final Report

This research project is a collaboration between the Sinskey laboratory at MIT and the Worden laboratory at Michigan State University. The goal of the project is to produce Isobutanol (IBT), a branched-chain alcohol that can serve as a drop-in transportation fuel, through the engineered microbial biosynthesis of Carbon Dioxide, Hydrogen, and Oxygen using a novel bioreactor. This final technical report presents the findings of both the biological engineering work at MIT that extended the native branched-chain amino acid pathway of the wild type Ralstonia eutropha H16 to perform this biosynthesis, as well as the unique design, modeling, and construction of a bioreactor for incompatible gasses at Michigan State that enabled the operational testing of the complete system. This 105 page technical report summarizing the three years of research includes 72 figures and 11 tables of findings. Ralstonia eutropha (also known as Cupriavidus necator) is a Gram-negative, facultatively chemolithoautotrophic bacteria. It has been the principle organism used for the study of polyhydroxybutyrate (PHB) polymer biosynthesis. The wild-type Ralstonia eutropha H16 produces PHB as an intracellular carbon storage material while under nutrient stress in the presence of excess carbon. Under this stress, it can accumulate approximately 80 % of its cell dry weight (CDW) as this intracellular polymer. With the restoration of the required nutrients, the cells are then able to catabolize this polymer. If extracted from the cell, this PHB polymer can be processed into biodegradable and biocompatible plastics, however for this research, it is the efficient metabolic pathway channeling the captured carbon that is of interest. R. eutropha is further unique in that it contains two carbon-fixation Calvin–Benson–Bassham cycle operons, two oxygen-tolerant hydrogenases, and several formate dehydrogenases. It has also been much studied for its ability in the presence of oxygen, to fix carbon dioxide into complex cellular molecules using the energy from hydrogen. In this research project, engineered strains of R. eutropha redirected the excess carbon from PHB storage into the production of isobutanol and 3-methyl-1-butanol (branched-chain higher alcohols). These branched-chain higher alcohols can be used directly as substitutes for fossil-based fuels and are seen as alternative biofuels to ethanol and biodiesel. Importantly, these alcohols have approximately 98 % of the energy content of gasoline, 17 % higher than the current gasoline additive ethanol, without impacting corn market production for feed or food. Unlike ethanol, these branched-chain alcohols have low vapor pressure, hygroscopicity, and water solubility, which make them readily compatible with the existing pipelines, gasoline pumps, and engines in our transportation infrastructure. While the use of alternative energies from solar, wind, geothermal, and hydroelectric has spread for stationary power applications, these energy sources cannot be effectively or efficiently employed in current or future transportation systems. With the ongoing concerns of fossil fuel availability and price stability over the long term, alternative biofuels like branched-chain higher alcohols hold promise as a suitable transportation fuel in the future. We showed in our research that various mutant strains of R. eutropha with isobutyraldehyde dehydrogenase activity, in combination with the overexpression of plasmid-borne, native branched-chain amino acid biosynthesis pathway genes and the overexpression of heterologous ketoisovalerate decarboxylase gene, would produce isobutanol and 3-methyl-1-butanol when initiated during nitrogen or phosphorus limitation. Early on, we isolated one mutant R. eutropha strain which produced over 180 mg/L branched-chain alcohols in flask culture while being more tolerant of isobutanol toxicity. After the targeted elimination of genes encoding several potential carbon sinks (ilvE, bkdAB, and aceE), the production titer of the improved to 270 mg/L isobutanol and 40 mg/L 3-methyl-1-butanol. Semicontinuous flask cultivation supplied the cells with sufficient nutrients while minimizing the toxicity caused by isobutanol. Under this cultivation, the R. eutropha mutant grew and produced more than 14 g/L branched-chain alcohols over the duration of 50 days. These results demonstrate that R. eutropha carbon flux can be redirected from PHB to branched-chain alcohols and that engineered R. eutropha can be cultivated over prolonged periods of time for product biosynthesis. While this bioengineering work was being done at the Sinskey laboratory at MIT, the researchers at the Worden laboratory at Michigan State were working on the design and construction of the required specialty bioreactor for incompatible gasses (BIG) that would allow the safe feeding of microbes on Carbon Dioxide, Hydrogen, and Oxygen without explosive results. The early design and assembly work in year 1 incorporated a novel microbubble generator to maximize the bioavailability of gasses within the system comprised of small scale hollow fiber reactors. The early success of the microbubble generator eliminated the need to investigate potentially toxic surfactants within the system. For operational control, the system design incorporated a Opto22-based control network. The researchers also selected the specific hollow fiber material suitable for the bioreactor application. A variety of commercially available hollow fiber membranes were compared with regard to their pore sizes, cell affinity, and potential interference to cell viability assays. The selected membrane with its spongy layer was then tested for diffusivity of O2 and CO2. The instrumented system was then fully assembled for experimentally measuring the heterotrophic growth rate of immobilized R. eutropha cells. The requisite procedures for inoculation, measurement, and cleaning were established enabling the system performance to be validated under controlled laboratory conditions. In year 2, the researchers completed the Opto22 based cross-platform control network, and the system’s communications across the Sartorius fermentation system and Bruker gas chromatograph was established via open platform communications (OPC) protocol. Using the revised system, measurements were taken of the R.eutropha cell growth rate and substrate mass transfer rate in the hollow fiber membrane. Several IBT recovery strategies were explored and resin adsorption was determined to be optimal solution for lab scale operations. The adsorption capacity of the resin column was then measured and IBT desorption using methanol has been demonstrated. With the growing body of experimental data in hand, mathematical models were constructed to demonstrate and map the cellular kinetics, mass transfer of heterotrophic and autotrophic substrates in the hollow fiber, and the adsorption process in the resin column. A structured kinetic model was constructed to describe the competition between cell mass generation and IBT production. The reactor was then scaled up from single fiber to a membrane area of 180 cm2 and then further to 1 ft2. In Year 3 of the research, the IBT mass transfer across the membrane was characterized within the system with experiments to empirically measure the IBT diffusion coefficient in the BIG spongy layer. Using the refined mathematical models, the researchers are now able to explain the experimental observations and predict bioreactor performance under a wide range of experimental conditions. The Big system is able to demonstrate continuous controlled operations with the integrated IBT recovery system. Both heterotrophic and autotrophic production have been shown during continuous operation with heterotrophic and autotrophic stages. Performance of BIG system has been measured during continuous run with alternating heterotrophic growth on fructose and autotrophic product formation on H2, CO2, and O2. Volumetric productivities of IBT at 325 mg/(L day) and of 3M1B at 50 mg/(L day) were achieved, which were comparable to that achieved under heterotrophic conditions. Using the mathematical model, researchers are able to predict system performance for scaled-up BIG system. The apparent diffusion coefficient of IBT in the spongy layer of XM-50 hollow fiber membranes has been measured at various lumen liquid flow rates. The experiment is simulated in COMSOL to validate the results. The constructed COMSOL model is able to simulate BIG system performance in both batch and continuous mode. Mathematical simulations of the system performance have been run to identify the most crucial operational conditions, identifying the rate-limiting factors in autotrophic production of IBT, and quantitating the rate of IBT catabolism. Investigations of the productivity of the production system have suggested and the modeling of the system has revealed a particular sensitivity to the catabolism of the produced IBT by the engineered R. eutropha. Experiments have been designed and executed to quantify the IBT catabolism of R. eutropha, which open up possibilities for further system improvements through future, targeted bioengineering of the strain. Finally, the researchers at Michigan State performed an economic analysis of the system, based on the collective results, and their findings are presented in full within the report.

08 HYDROGEN↗

Argonne National Laboratory Annual Report of Laboratory Directed Research and Development Program Activities FY 2012

Our plans to upgrade the Advanced Photon Source are proceeding on schedule, and we hope to receive CD-2 approval sometime in spring 2013. When complete, the APS Upgrade will equip this vital facility for continued scientific leadership in the 21st century, providing the 5,000-plus members of our user community with greatly increased time, energy and spatial resolution. We also have successfully installed “Mira,” the new IBM Blue Gene/Q system at the Argonne Leadership Computing Facility. The world’s fourth-fastest supercomputer, Mira is now providing leading-edge computational power across all scientific disciplines. I am also pleased to report that Argonne’s new Energy Sciences Building is proceeding on schedule; we expect it to be finished and ready for occupancy by June 30, 2013. When complete, the ESB will be one of the most modern and most flexible energy labs in the world, creating a new home for researchers focused on a wide range of energy science challenges. Research is under way at the new multidisciplinary Institute for Molecular Engineering (IME), a joint program of the University of Chicago and Argonne. Over the past year, four new faculty members, including IME’s director, – all pre-eminent researchers who hold joint appointments at Argonne – have joined the IME. We look forward to working in collaboration with the IME to find new ways to address the interfaces between molecular-level science and powerful new technologies. In FY 2012, we continued support for the final year of the “Beyond Lithium-Ion” Director’s Grand Challenge LDRD project. That project enabled significant work on the performance of lithium-based energy storage systems, pointing the way to new high-energy-density lithium-air batteries. I believe this Grand Challenge contributed significantly to our successful bid to win designation of the Argonne-based Joint Center for Energy Storage Research (JCESR) as the DOE’s new Batteries and Energy Storage Hub. JCESR, which brings together a world-class “dream team” of national laboratories, universities and private sector partners, strongly benefited from LDRD program investments, both in FY2012 and in previous years. As we move forward in FY2013, the Director’s Grand Challenge is now focused on new ways to address the challenges of “big data” – the acquisition, storage, transfer, and analysis of both experimental and simulated data at scales approaching the limits of our current computational capacities. In managing Argonne’s LDRD program, we have adhered to the requirements of DOE Order 413.2b and associated guidelines. Our program management continually strives for increased efficiency. In addition to meeting all reporting requirements during FY 2012, our LDRD Program Office continues to implement intranet-based process enhancements to better serve all LDRD stakeholders, including our DOE Site Office colleagues. As the following individual project reports demonstrate, Argonne’s researchers are pursuing projects at the leading edge of science and engineering, and making significant contributions to the advancement of our laboratory’s major strategic initiatives. Evidence of the continuing strength of this program is found in the follow-on sponsorship won by many of these research projects, as well as in the numbers of publications, invited presentations, inventions, and new staff hires that are direct results of LDRD. LDRD enables exploration of new scientific directions and makes it possible to adapt quickly to a changing landscape with unexpected opportunities, while enhancing the capabilities of our researchers and our scientific facilities. For these reasons, LDRD remains an invaluable resource to address the future science and technology needs of DOE, and of our nation.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

High‐Resolution National‐Scale Water Modeling Is Enhanced by Multiscale Differentiable Physics‐Informed Machine Learning

Abstract The National Water Model (NWM) is a key tool for flood forecasting, planning, and water management. Key challenges facing the NWM include calibration and parameter regionalization when confronted with big data. We present two novel versions of high‐resolution (∼37 km 2 ) differentiable models (a type of hybrid model): one with implicit, unit‐hydrograph‐style routing and another with explicit Muskingum‐Cunge routing in the river network. The former predicts streamflow at basin outlets whereas the latter presents a discretized product that seamlessly covers rivers in the conterminous United States (CONUS). Both versions use neural networks to provide a multiscale parameterization and process‐based equations to provide a structural backbone, which were trained simultaneously (“end‐to‐end”) on 2,807 basins across the CONUS and evaluated on 4,997 basins. Both versions show great potential to elevate future NWM performance for extensively calibrated as well as ungauged sites: the median daily Nash‐Sutcliffe efficiency of all 4,997 basins is improved to around 0.68 from 0.48 of NWM3.0. As they resolve spatial heterogeneity, both versions greatly improved simulations in the western CONUS and also in the Prairie Pothole Region, a long‐standing modeling challenge. The Muskingum‐Cunge version further improved performance for basins >10,000 km 2 . Overall, our results show how neural‐network‐based parameterizations can improve NWM performance for providing operational flood predictions while maintaining interpretability and multivariate outputs. The modeling system supports the Basic Model Interface (BMI), which allows seamless integration with the next‐generation NWM. We also provide a CONUS‐scale hydrologic data set for further evaluation and use.

Song, Yalan [Civil and Environmental Engineering T↗

A Scalable Graph Analytics Framework for Programming with Big Data in R (pbdR)

Many disciplines such as biology, economics, engineering, physics, and the social sciences represent their data as graphs to capture patterns, trends, and associations. There are are many commercially available graph libraries in different programming languages to analyze these complex graphs. But there is no distributed graph library package in R - the popular statistical programming language to analyze graphs that bigger than a single machine's memory. Many domain experts prefer R over the numerous other alternatives. Towards this, we present a distributed graph analytics framework for R called programming with big graph using R (pBGR.) Our proposed framework leverages the Programming with Big Data in R (pbdR) ecosystem that provides scalable R packages for distributed computing in data science. We present an early prototype implementation of this framework using the distributed-memory parallel graph library CombBLAS and evaluate the framework's performance on leadership class computing platforms. Our experimental results demonstrate that the proposed framework is capable of performing large-scale parallel graph mining through the easyto-use R language. This enhanced graph processing capability coupled with other statistical tools already available in R, should be valuable to many domain experts.

Hasan, S M Shamimul↗

A Control Chart Approach for Representing and Mining Data Streams with Shape Based Similarity

The mining of data streams for online condition monitoring is a challenging task in several domains including (electric) power grid system, intelligent manufacturing, and consumer science. Considering a power grid application in which thousands of sensors, called the phasor measurement units, are deployed on the power grid network to continuously collect streams of digital data for real-time situational awareness and system management. Depending on design, each sensor could stream between ten and sixty data samples per second. The myriad of sensory data captured could convey deeper insights about sequence of events in real-time and before major damages are done. However, the timely processing and analysis of these high-velocity and high-volume data streams is a challenge. Hence, a new data processing and transformation approach, based on the concept of control charts, for representing sequence of data streams from sensors is proposed. In addition, an application of the proposed approach for enhancing data mining tasks such as clustering using real-world power grid data streams is presented. The results indicate that the proposed approach is very efficient for data streams storage and manipulation.

big data↗

SGAS 143845.1+145407: A BIG, COOL STARBURST AT REDSHIFT 0.816

We present the discovery and detailed multi-wavelength study of a strongly lensed luminous infrared galaxy at z = 0.816. Unlike most known lensed galaxies discovered at optical or near-infrared wavelengths, this lensed source is red, (r - K{sub s} ) {sub AB} = 3.9, which the data presented here demonstrate is due to ongoing dusty star formation. The overall lensing magnification (a factor of 17) facilitates observations from the blue optical through to 500 {mu}m, fully capturing both the stellar photospheric emission and the re-processed thermal dust emission. We also present optical and near-IR spectroscopy. These extensive data show that this lensed galaxy is in many ways typical of IR-detected sources at z {approx} 1, with both a total luminosity and size in accordance with other (albeit much less detailed) measurements for samples of galaxies observed in deep fields with the Spitzer telescope. Its far-infrared spectral energy distribution is well fit by local templates that are an order of magnitude less luminous than the lensed galaxy; local templates of comparable luminosity are too hot to fit. Its size (D {approx} 7 kpc) is much larger than local luminous infrared galaxies, but in line with sizes observed for such galaxies at z {approx} 1. The star formation appears uniform across this spatial scale. In this source, the luminosity of which is typical of sources that dominate the cosmic infrared background, we find that star formation is spatially extended and well organized, quite unlike the compact merger-driven starbursts that are typical for sources of this luminosity at z {approx} 0.

79 ASTRONOMY AND ASTROPHYSICS↗

Global gross primary production simulated by three terrestrial biosphere models to analyse smoothing of limiting rates

Processed global terrestrial gross primary production (GPP) from three terrestrial biosphere models (TBMs). Two simulations were run with each model, one that selects the rate limiting process of photosynthesis based on the minimum of the potential rates (following Farquhar et al. 1980) and the other that uses quadratic smoothing between the potential rates (following Collatz et al. 1991). The three TBMs are: 1) the Energy Exascale Earth System Model (E3SM) land model (ELM) (release: v1.1.0) (Burrows et al., 2020), a coupled carbon, nitrogen, and phosphorus model with sun/shade big-leaf canopy photosynthesis scaling. 2) The Functionally Assembled Terrestrial Ecosystem Simulator (FATES, tag: sci.1.30.0_api.8.0.0) (Koven et al., 2019), coupled with the Community Land Model (CLM, version 5; Lawrence et al., 2019), a carbon-only vegetation demography model with multi-leaf and multi-canopy layers for scaling photosynthesis, and with a leaf area index (LAI) optimisation scheme. 3) The Sheffield Dynamic Global Vegetation Model (SDGVM, tag: Walkeretal2020_GCB), a carbon-only model with multi-leaf-layer canopy photosynthesis scaling and also with an LAI optimisation scheme (Walker et al., 2017; Woodward & Lomas, 2004). These data were generated to evaluate the effect of Collatz smoothing of limiting rates on simulated GPP across a range of terrestrial biosphere models. Analysis of results can be found in Walker et al. (2020). This dataset is comprised of R scripts to process data and produce figures and .RDS files, a binary format specific to R and read with the 'readRDS' function.

54 ENVIRONMENTAL SCIENCES↗

Using Likwid and Byfl to Benchmark Hardware Performance

This paper outlines a benchmarking study conducted during my internship at LANL, focusing on CPU (Computer Processing Unit) and program performance assessment. The primary goal was to gather memory access data using three methods across five polybench kernels The data gathered would then be used to compare and contrast to one another and calculate operational intensity for performance comparisons. Benchmarking tools like Byfl and Likwid were employed, with Byfl offering hardware-independent data through LLVM compiler communication and Likwid directly interacting with computer hardware. The study considered various benchmarking factors, including optimization levels, Big O notation ((n)), CPU diversity and specific kernel equations. Big O notation was utilized to simplify code complexity, with detailed breakdwons of operations and memory components for each polybench application. Specific O(n) equations enabled nuanced kernel compariosns, facilitating the identification of performance variations. CPU efficiency assessments were conducted using Likwid tests on two CPUs. The central focus on code optimization aimed at achieving higher speeds and reduced memory usage through streamlined code. Future work propsoes creating a roofline model, synthesizing benchmarking data into a comprehensive data graph to assist in optimizing code and improving hardware performance. The potential impact on the laboratory or national mission was underscored, emphasizing the importance of optimizing applications and hardware to conserve resources and accelerate program execution. The specific relevance to LANL’s operations in math-intensive fields such as Nuclear Fission, Space Exploration, and Nanotechnology highlights the necessity of efficient benchmarking for resource conservation and proram speed. Overall, this study contributes to the understanding of CPU and program performance, providing insights for future optimization efforts in a laboratory setting

97 MATHEMATICS AND COMPUTING↗

An Interactive Visualization Tool for Large-Scale Building Stock Modeling: Preprint

Recent advancements in data science and high-performance computing are making it easier to run millions of building simulations, but meaningful visualization of such large datasets remains a challenge. This paper presents a new tool developed to view the results of large-scale OpenStudio simulations of national, regional, or local building stocks. The tool processes millions of simulations to calculate measure savings, utility bills, carbon emissions, primary energy, and cost-effectiveness metrics at a high geographic resolution. Interactive visualizations of the building characteristics, consumption, and measure savings data include proportional symbol maps and histogram plots and can be filtered by any building characteristic.

big data↗

A Multi-Branch Decoder Network Approach to Adaptive Temporal Data Selection and Reconstruction for Big Scientific Simulation Data

A key challenge in scientific simulation is that the simulation outputs often require intensive I/O and storage space to store the results for effective post hoc analysis. This article focuses on a quality-aware adaptive temporal data selection and reconstruction problem where the goal is to adaptively select simulation data samples at certain key timesteps in situ and reconstruct the discarded samples with quality assurance during post hoc analysis. This problem is motivated by the limitation of current solutions that a significant amount of simulation data samples are either discarded or aggregated during the sampling process, leading to inaccurate modeling of the simulated phenomena. Two unique challenges exist: 1) the sampling decisions have to be made in situ and adapted to the dynamics of the complex scientific simulation data; 2) the reconstruction error must be strictly bounded to meet the application requirement. To address the above challenges, we develop DeepSample , an error-controlled convolutional neural network framework, that jointly integrates a set of coherent multi-branch deep decoders to effectively reconstruct the simulation data with rigorous quality assurance. The results on two real-world scientific simulation applications show that DeepSample significantly outperforms other state-of-the-art methods on both sampling efficiency and reconstructed simulation data quality.

Zhang, Yang↗