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Comparative evaluation of deep learning workloads for leadership-class systems
Deep learning (DL) workloads and their performance at scale are becoming important factors to consider as we design, develop and deploy next-generation high-performance computing systems. Since DL applications rely heavily on DL frameworks and underlying compute (CPU/GPU) stacks, it is essential to gain a holistic understanding from compute kernels, models, and frameworks of popular DL stacks, and to assess their impact on science-driven, mission-critical applications. At Oak Ridge Leadership Computing Facility (OLCF), we employ a set of micro and macro DL benchmarks established through the Collaboration of Oak Ridge, Argonne, and Livermore (CORAL) to evaluate the AI readiness of our next-generation supercomputers. In this paper, we present our early observations and performance benchmark comparisons between the Nvidia V100 based Summit system with its CUDA stack and an AMD MI100 based testbed system with its ROCm stack. We take a layered perspective on DL benchmarking and point to opportunities for future optimizations in the technologies that we consider.
Comparative Analysis of Corrinoid Profiles across Host-Associated and Environmental Samples
Vitamin B 12 (the cyanated form of cobalamin cofactors) is best known for its essential role in human health. In addition to its function in human metabolism, cobalamin also plays important roles in microbial metabolism and can impact microbial community function. Cobalamin is a member of the structurally diverse family of cofactors known as cobamides that are produced exclusively by certain prokaryotes. Cobamides are considered shared nutrients in microbial communities because the majority of bacteria that possess cobamide-dependent enzymes cannot synthesize cobamides de novo. Furthermore, different microbes have evolved metabolic specificity for particular cobamides, and therefore, the availability of cobamides in the environment is important for cobamide-dependent microbes. Determining the cobamides present in an environment of interest is essential for understanding microbial metabolic interactions. By examining the abundances of different cobamides in diverse environments, including 10 obtained in this study, we find that, contrary to its preeminence in human metabolism, cobalamin is relatively rare in many microbial habitats. Comparison of cobamide profiles of mammalian gastrointestinal samples and wood-feeding insects reveals that host-associated cobamide abundances vary and that fecal cobamide profiles differ from those of their host gastrointestinal tracts. Environmental cobamide profiles obtained from aquatic, soil, and contaminated groundwater samples reveal that the cobamide compositions of environmental samples are highly variable. As the only commercially available cobamide, cobalamin is routinely supplied during microbial culturing efforts. Furthermore, these findings suggest that cobamides specific to a given microbiome may yield greater insight into nutrient utilization and physiological processes that occur in these habitats.
Comparing coordination uranyl( vi ) complexes with 2-(1 H -imidazo[4,5- b ]phenazin-2-yl)phenol and derivatives
The synthesis, characterization, emission, and electronic spectroscopy for complexes of derivatives of 2-(1 H -imidazo[4,5- b ]phenazin-2-yl)phenol with uranyl (UO 2 2+ ) and copper (Cu 2+ ) are detailed.
Comparing reproduced cyber experimentation studies across different emulation testbeds.
Abstract not provided.
Comparative PV LCOE Calculator (Comparative Photovoltaic Levelized Cost of Energy Calculator) [SWR-20-86]
The calculator calculates levelized cost of energy (LCOE) for photovoltaic (PV) systems based on cost, performance, and reliability inputs for a baseline and a proposed technology. The user can choose inputs and watch the effect on LCOE to determine whether a proposed technology is cost-effective, perform trade-off analysis among different technology options, and do break-even analysis of cost or performance.
An Analysis Technique/Automated Tool for Comparing and Tracking Analysis Modes of Different Finite Element Models
An analysis technique was developed to compare and track mode shapes for different Finite Element Models. The technique may be applied to a variety of structural dynamics analyses, including model reduction validation (comparing unreduced and reduced models), mode tracking for various parametric analyses (e.g., launch vehicle model dispersion analysis to identify sensitivities to modal gain for Guidance, Navigation, and Control), comparing models of different mesh fidelity (e.g., a coarse model for a preliminary analysis compared to a higher-fidelity model for a detailed analysis) and mode tracking for a structure with properties that change over time (e.g., a launch vehicle from liftoff through end-of-burn, with propellant being expended during the flight). Mode shapes for different models are compared and tracked using several numerical indicators, including traditional Cross-Orthogonality and Modal Assurance Criteria approaches, as well as numerical indicators obtained by comparing modal strain energy and kinetic energy distributions. This analysis technique has been used to reliably identify correlated mode shapes for complex Finite Element Models that would otherwise be difficult to compare using traditional techniques. This improved approach also utilizes an adaptive mode tracking algorithm that allows for automated tracking when working with complex models and/or comparing a large group of models.
Video event trigger and tracking system using fuzzy comparators
A video observation method and apparatus, the apparatus having a frame storage mechanism, a dividing mechanism, a plurality of fuzzy comparators and a trigger signal mechanism. The frame storage mechanism stores at least one non-current video frame of a viewing field. The dividing mechanism divides a current video frame of the viewing field and the at least one non-current video frame into a plurality of corresponding trigger sections. The plurality of fuzzy comparators each compare and detect a fuzzy logic difference between one trigger section of the current video frame and the corresponding trigger sections of the at least one non-current video frame, the number of fuzzy comparators being selected so that every trigger section of the current video frame is compared. The trigger signal mechanism provides a trigger signal when a fuzzy logic difference is detected between any of the corresponding current and non-current trigger sections. A video observation mechanism and data reducing mechanism may be included with the above apparatus or alone with only a frame storage mechanism, a single generic comparator and a trigger signal mechanism. The video observation mechanism provides a video data stream, wherein each pixel of each frame of a viewing field is provided as multiple bits of data. The data reducing mechanism reduces each set of multiple bits of data which correspond to each pixel to one bit of binary data based on whether the pixel has a level of grey which is above or below a threshold level of grey.
Off-spec fly ash-based lightweight aggregate properties and their influence on the fresh, mechanical, and hydration properties of lightweight concrete: A comparative study
An off-spec fly ash-based spherical lightweight aggregate (LWA), designated as Spherical Porous Reactive Aggregate (SPoRA), was manufactured through a lab pilot-scale production and its engineering properties, including specific gravity, dry rodded unit weight, water absorption, mechanical performance, and pore structure, were evaluated. Using SPoRA, lightweight concrete (LWC) samples were made and their fresh, mechanical, and hydration properties were assessed and compared with LWC samples made using two commercial LWA available in the US market. The results indicated that fine and coarse SPoRA had 72 h water absorption capacities of 16.4 % and 20.9 %, respectively, which were higher than that of the two commercial LWAs. Higher saturated surface dry specific gravity of SPoRA compared to commercial LWAs led to a higher fresh density for the corresponding LWC. Using X-ray computed tomography, large spherical type pores were observed in SPoRA similar to those in the commercial slate-based LWA. Additionally, the pore size distribution of SPoRA, characterized by a dynamic vapor sorption analyzer, indicated that more than 97 % of the pores had diameters greater than 50 nm. SPoRA’s average bulk crushing strength was 6.8 MPa which was smaller than commercial LWA and was potentially attributed to the differences in manufacturing processes. Nonetheless, SPoRA LWC with 28 days compressive strength of 29 MPa passed the ASTM C330 requirement and had a comparable strength with LWC prepared with the commercial LWAs indicating lesser importance for the LWA strength. SPoRA LWC had a flowability of 5 % compared to LWC prepared with commercial LWAs having flowability of 18 %. This observation was potentially related to the fluxing agent used in the SPoRA production. SPoRA had a 70 % degree of hydration at 12 days which was comparable with that of LWC prepared with commercial LWA.
Low-carbon fuels for spark-ignited engines: A comparative study of compressed natural gas and liquefied petroleum gas on a CFR engine with exhaust gas recirculation
Decades of work on low-carbon fuels have established their potential for substantial emissions reductions; however, their adoption is still limited by infrastructure concerns and engine efficiency deficits. As infrastructures have begun to evolve, research on strategies that maximize engine efficiency through interactions with fuel properties must also now take center place. This paper compares the performance, emissions, and combustion characteristics of two forefront low-carbon fuels: compressed natural gas (CNG) and liquefied petroleum gas (LPG) in a cooperative fuel research (CFR) engine over a range of compression ratios and engine loads. The effects of exhaust gas recirculation (EGR), end-gas auto-ignition, and a novel combustion control tool, the combustion intensity metric (CIM), were also evaluated at different stoichiometric engine operating conditions. In comparison to LPG, CNG operation demonstrated an extended knock-free regime, allowing engine operation at higher engine loads and compression ratios, but LPG operation exhibited enhanced combustion characteristics with higher peak pressures and faster apparent heat release rates (AHRR). LPG operation achieved higher brake thermal efficiencies and lower equivalent CO 2 emissions compared to CNG operation at the tested engine loads and compression ratios. LPG demonstrated significantly higher EGR tolerance limits compared to CNG, with a maximum of 28% EGR rate, compared to 23% for CNG. This improved EGR dilution tolerance was responsible for a 90% reduction in NOx emissions for LPG compared to a maximum of 70% with CNG. EGR dilution also exhibited more effective knock mitigation potential with LPG, suppressing knock intensity values by up to 98% and transitioning the engine operation towards normal combustion from heavy knocking conditions. As a result, the CIM was found to decrease burn durations and improve the quality of combustion by controlling the desired fraction of end-gas auto-ignition.
Comparability of Liquid Chromatography Tandem Mass Spectrometry Analysis of Dissolved Organic Matter across Laboratories
Non-targeted liquid chromatography tandem highresolution mass spectrometry (LC−MS/MS) is increasingly applied for the structure-resolved chemical analysis of dissolved organic matter (DOM). With new developments in MS instrumentation and analysis software, the approach has gained substantial momentum over the past decade. However, achieving high-quality analytical data that is reproducible and comparable across laboratories can be a bottleneck in non-targeted metabolomics and organic matter chemical analysis, especially for data reuse in repository-scale analyses. Understanding the capabilities as well as challenges of comparing LC−MS/MS data from different laboratories is necessary for inferring global trends from public data sets. To illuminate instrumentation factors that drive differences and variability, we used a standardized data analysis pipeline, including classical (CMN) and featurebased molecular networking (FBMN), to analyze data from a ring trial by 24 laboratories on identical sample sets of algal and DOM extracts that were mixed in predefined concentrations and spiked with standards. Our results showed that data sets from similar mass spectrometer types with unified instrument parameters were qualitatively comparable, resolving the same general trends and shared mass spectral features. Interlaboratory comparability was best for high-intensity features, while low-intensity features showed greater detection variability. Our analysis also highlights challenges when comparing data from instruments with different acquisition rates or operating with less standardized methods. Lastly, we provide recommendations for data integration, public data sharing, standardization, and best practices for standardized LC−MS/MS data acquisition, which will be critical for long-term time series and intercomparability of DOM chemical analyses.
Comparing event generator predictions and ab initio calculations of ν- 12 C neutral-current quasielastic scattering at 1 GeV
The measurement of neutrino oscillations and exotic physics searches are important parts of the physics program in the near future, with new state-of-the-art experiments planned within the next decade. Future and modern experiments in these fields will make use of nuclear targets. Event generators (EGs) are software used in the analysis of neutrino oscillation experiments. EGs are used to predict kinematic observables for a range of neutrino energies. These simulations make use of simple models of nucleon dynamics. As such, they may fail to capture features of more rigorous theoretical calculations. This work compares EG performance to nuclear theory calculations by comparing observables generated in the two frameworks. We provide a common set of definitions between theory and experiment and assess the adequacy of the implementation of two nuclear physics models in EG simulations. Neutral-current quasielastic (NCQE) scattering events for neutrinos and antineutrinos on a 12 C target are simulated with a specific EG, NEUT, used by the T2K experiment for its analysis. The simulated cross sections are compared to analytic calculations from nuclear theory within the factorization scheme. We compare the NEUT implementation of two different models on nuclear spectral functions: the relativistic Fermi gas (RFG) and the correlated basis spectral function (CBF) to analytic calculations of the same models in the factorization scheme. For both nuclear physics models, we compare the appearance of features in the distributions relevant to experimental analyses. The peak of the cross section dσ/(dΩ dω) is consistent in energy transfer, ω, for RFG and CBF simulations. Qualitatively, the shape of the simulated distribution is similar to the one obtained through theory calculations; however, there are some discrepancies between the theory calculations and the NEUT simulation. While the EG simulations and analytic calculations with the same model of nuclear dynamics show similar overall features, there are still differences between the two. These results demonstrate the importance of benchmarking EGs so their nuclear physics implementations can be improved for the analysis of future experiments.
Impact of 12-nm FinFET Technology Variations on TID Effects: A Comparative Study of GF 12LP and 12LP+ at the Transistor Level
Here, this article presents a comparative analysis of total ionizing dose (TID) response in GlobalFoundries’ (GF) 12 low-power (LP) and 12LP+12-nm bulk fin field effect transistor (FinFET) technologies using 10-keV X-rays. Our findings show that 12LP+ n-type transistors demonstrate higher sensitivity to TID degradation of the off-state leakage drain current compared to 12LP. Data indicate that for both 12LP and 12LP+, transistors with higher threshold voltages (VTs) exhibit lower off-state drain-source leakage postirradiation compared to transistors with lower VTs. Data consistently show that transistors with fewer fins per transistor show superior TID tolerance, in both 12LP and 12LP+ technologies. Lower VT transistors in both technologies display similar preirradiation leakage currents. On the other hand, higher VT transistors in 12LP+ show lower preirradiation leakage currents than those in 12LP, highlighting that the front-end-of-line of 12LP+ technology has been modified compared to 12LP. p-type devices in 12LP+ presented negligible degradation. Larger TID sensitivity in 12LP+ might be attributed to the implementation of dual-metal gate work functions, reduced halo doping, deeper source/drain (S/D) doping profiles, and/or 12LP+ having narrower fins compared to 12LP.
Graph-Based Similarity Metrics for Comparing Simulation Model Causal Structures
The causal structure of a simulation is a major determinant of both its character and behavior, yet most methods we use to compare simulations focus only on simulation outputs. We introduce a method that combines graphical representation with information theoretic metrics to quantitatively compare the causal structures of models. The method applies to agent-based simulations as well as system dynamics models and facilitates comparison within and between types. Comparing models based on their causal structures can illuminate differences in assumptions made by the models, allowing modelers to (1) better situate their models in the context of existing work, including highlighting novelty, (2) explicitly compare conceptual theory and assumptions to simulated theory and assumptions, and (3) investigate potential causal drivers of divergent behavior between models. We demonstrate the method by comparing two epidemiology models at different levels of aggregation.
Performance Monitoring Program: Developing Comparative Metrics for Fitness-for-Duty Programs
To comply with U.S. Nuclear Regulatory Commission (NRC) regulations, licensees and entities authorized under Title 10 of the Code of Federal Regulations (CFR) Part 26 Section 26.3 (§ 26.3) are required to have fitness-for-duty (FFD) programs. Under § 26.3, the expectation of these FFD programs is to provide reasonable assurance that individuals who are granted unescorted access to nuclear power reactor protected areas and Category I fuel cycle facility material control areas are trustworthy, will perform their tasks in a reliable manner, are not under the influence of any substance, legal or illegal, that may impair their ability to perform their duties, and are not mentally or physically impaired from any cause that can adversely affect their ability to safely and competently perform their duties. Pacific Northwest National Laboratory (PNNL) was tasked with developing a performance monitoring program to risk inform NRC inspection and policy regarding quantitative FFD performance data. To meet this need, PNNL developed methodologies that could be implemented within a performance monitoring program. Throughout this report, these methodologies are referred to as comparative metrics. The NRC provided PNNL with 2016–2019 data from annual reporting forms and single positive test forms provided by licensees and other entities. For most of the comparative metrics, the analyses required customized processing, such as creating filtering fields, adding data fields and summaries, and joining datasets. The comparative metrics developed include: random testing rate, random policy violation rate, pre-access policy violation rate, subversion attempt rate, and number of policy violations by labor category. The comparative metrics can be used for parsing and visualizing FFD program data and monitoring FFD performance at the labor category, facility, licensee, and industry levels to risk inform NRC inspection and policy with regard to the FFD data currently collected from licensees and other entities that implement Part 26 requirements. Furthermore, these developed comparative metrics allow a more in-depth look at the FFD programs for the industry to discern trends and patterns that may warrant changes at an industry level and to inform policy decisions. These analyses should be refreshed as new FFD data become available.
Lessons from an evolving rRNA: 16S and 23S rRNA structures from a comparative perspective
The 16S and 23S rRNA higher-order structures inferred from comparative analysis are now quite refined. The models presented here differ from their immediate predecessors only in minor detail. Thus, it is safe to assert that all of the standard secondary-structure elements in (prokaryotic) rRNAs have been identified, with approximately 90% of the individual base pairs in each molecule having independent comparative support, and that at least some of the tertiary interactions have been revealed. It is interesting to compare the rRNAs in this respect with tRNA, whose higher-order structure is known in detail from its crystal structure (36) (Table 2). It can be seen that rRNAs have as great a fraction of their sequence in established secondary-structure elements as does tRNA. However, the fact that the former show a much lower fraction of identified tertiary interactions and a greater fraction of unpaired nucleotides than the latter implies that many of the rRNA tertiary interactions remain to be located. (Alternatively, the ribosome might involve protein-rRNA rather than intramolecular rRNA interactions to stabilize three-dimensional structure.) Experimental studies on rRNA are consistent to a first approximation with the structures proposed here, confirming the basic assumption of comparative analysis, i.e., that bases whose compositions strictly covary are physically interacting. In the exhaustive study of Moazed et al. (45) on protection of the bases in the small-subunit rRNA against chemical modification, the vast majority of bases inferred to pair by covariation are found to be protected from chemical modification, both in isolated small-subunit rRNA and in the 30S subunit. The majority of the tertiary interactions are reflected in the chemical protection data as well (45). On the other hand, many of the bases not shown as paired in Fig. 1 are accessible to chemical attack (45). However, in this case a sizeable fraction of them are also protected against chemical modification (in the isolated rRNA), which suggests that considerable higher-order structure remains to be found (although all of it may not involve base-base interactions and so may not be detectable by comparative analysis). The agreement between the higher-order structure of the small-subunit rRNA and protection against chemical modification is not perfect, however; some bases shown to covary canonically are accessible to chemical modification (45).(ABSTRACT TRUNCATED AT 400 WORDS).
ncompare: A Python Package for Comparing netCDF Structures
Earth science researchers and data engineers have a common problem: they often need to compare data files to see what is different between them. A lot of time is spent developing code to test differences. When it comes to comparing multidimensional data file formats like netCDFs (Network Common Data Form), this is particularly challenging and time-consuming, since there is frequently a need to evaluate the differences between dimension sizes, variable structures, and variable attributes, especially for regression testing. Since netCDFs are widely used in Earth science — with climate models, oceanographic or atmospheric reanalyses, and observational data — improved means of evaluating netCDF files can help enable a wide range of applications. We have developed a reusable open source approach through `ncompare`, which is a Python package for comparing netCDF structures [[https://github.com/nasa/ncompare]]. The `ncompare` tool compares the structure of two Network Common Data Form (NetCDF) files at the command line. It facilitates rapid comparisons by generating a formatted display of the matching and non-matching groups, variables, and associated metadata between two NetCDF datasets. The user has the option to colorize the terminal output for ease of viewing, and `ncompare` can optionally save comparison reports in text, comma-separated value (CSV), and/or Microsoft Excel formats. Despite the availability of tools (such as ncmpidiff or nccmp) that compare the values of variables, there was not previously a readily available, Python-based tool for rapid visual comparisons of group and variable structures, attributes, and chunking. `ncompare` was developed at NASA’s Atmospheric Science Data Center (ASDC) and is a collaboration with NASA Openscapes [[https://nasa-openscapes.github.io]] mentors across 11 of NASA’s data centers. Openscapes’ overarching vision is to support scientific researchers using NASA Earthdata as they migrate their workflows to the cloud. Relevant links: - https://github.com/nasa/ncompare - https://github.com/pyOpenSci/software-submission/issues/146 - https://nasa-openscapes.github.io