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In this work, we present JACC.shared, a new feature of Julia for ACCelerators (JACC), which is the performanceportable and metaprogramming model of the just-in-time and LLVM-based Julia language. This new feature allows JACC applications to leverage the high-performance computing (HPC) capabilities of high-bandwidth, on-chip GPU memory. Historically, exploiting high-bandwidth, shared-memory GPUs has not been a priority for high-level programming solutions. JACC.shared covers that gap for the first time, thereby providing a highlevel, portable, and easy-to-use solution for programmers to exploit this memory and supporting all current major accelerator architectures. Well-known HPC and AI workloads, such as multi/hyperspectral imaging and AI convolutions, have been used to evaluate JACC.shared on two exascale GPU architectures hosted by some of the most powerful US Department of Energy supercomputers: Perlmutter (NVIDIA A100) and Frontier (AMD MI250X). The performance evaluation reports speedup of up to 3.5× by adding only one line of code to the base codes, thus providing important accelerators in a simple, portable, and transparent way and elevating the programming productivity and performance-portability capabilities for Julia/JACC HPC, AI, and scientific applications.
A self-powered IoT system-on-chip (SoC) reduces power to sub-μw and employs multiple power-management techniques to trade-off ultra-low power (ULP), higher performance, smaller energy harvester footprint, and longer operating lifetime. Minimum Energy Point Tracking (MEPT) [1]–[4] keeps an SoC operating at the minimum energy point (MEP) to enhance system lifetime. Previous sample-and-hold MEPT schemes need frequent voltage comparisons and a high-frequency clock that increases power [2]. Current-ratio-based MEPT relies on specialized CMOS technology for body-bias tuning [3]. A switched-capacitor-based MEPT can achieve energy minimization at a targeted performance [4], but it uses a 30MHz clock witμW power consumption and low power efficiency. For ULP IoT applications, SoCs need to have ultra-low quiescent power, high efficiency for energy delivery, performance scaling based on available energy, and energy minimization to increase system lifetime. In this work, we propose an ULP IoT SoC with a triple-mode power management unit (PMU) that integrates energy-performance scaling, event-driven fast DVFS, and MEPT features to improve the system energy efficiency, as shown in Fig. 13.8.1. This work achieves a minimum 194nW power consumption for the SoC and 5.2nW quiescent power for the PMU with a 92.6% peak efficiency and >10 4 dynamic range. The timing waveform in Fig. 13.8.1 (bottom), demonstrates the transition of the three modes including energy aware (EA), performance aware (PA), and MEPT based on event priority and input voltage level which reflects the energy availability. As such, the system energy consumption and performance could be well-balanced based on both the input and output conditions.
Federated machine learning (FL) is gaining significant popularity to develop cybersecurity solutions in power grids because of its advanced capability to support decentralized data handing at local devices, its privacy preservation, and its low-bandwidth requirement. However, the evolving adversarial machine learning (AML) threats raise significant concerns for the cybersecurity of FL architectures. The FL-based split neural network (SplitNN) achieves high performance through the decentralized training of local neural network models while preserving data privacy across multiple entities. In this paper, we propose a methodology for evaluating the performance of a vertical FLbased anomaly detector against different types of AML attacks, including denial-of-service attacks, adversarial data injection attacks, and replay attacks on the trained local models deployed in the grid network. For a case study, we consider the modified IEEE 13-bus system, and we develop SplitNN-based binary and multiclass classification models to detect, locate, and identify different types of data integrity attacks on the volt-watt control with two pooling layers: maximum pooling and AvgPool. Our experimental results, computed through performance metrics, reveal that the severity of these AML attacks varies with the integrated pooling mechanism, the type of classification model, and the nature of the cyberattack. Further, the AML attacks negatively impacted the prediction time per sample for the pretrained SplitNN during the online testing.
We present JACC (Julia for Accelerators), the first high-level, and performance-portable model for the just-in-time and LLVM-based Julia language. JACC provides a unified and lightweight front end across different back ends available in Julia, enabling the same Julia code to run efficiently on many HPC CPU and GPU targets. We evaluated the performance of JACC for common HPC kernels as well as for the most computationally demanding kernels used in applications, HPCCG, a supercomputing benchmark test for sparse domains, and HARVEY, a blood flow simulator to assist in the diagnosis and treatment of patients suffering from vascular diseases. We carried out the performance analysis on the most advanced US DOE supercomputers: Aurora, Frontier, and Perlmutter. Overall, we show that JACC has a negligible overhead versus vendor-specific solutions, reporting GPU speedups with no extra cost to programmability.
Concentrating Solar Power (CSP) generation is an attractive option for low-emission power generation; however, the high costs of thermal storage associated with concentrating solar create a large barrier for their use and adaptation into modern life. Lowering their operation costs, while maintaining high thermal storage and transfer performance is essential. Solid particle-based heat exchange systems can reduce CSP cost but are often less efficient. Efforts to increase their performance have led to use of binary size particle mixes. Presented is an optical-based thermal analysis technique used to measure near-wall thermal conductivity of particle beds essential in determining their heat exchanger efficiency. Modulated Photothermal Radiometry is used to make dynamic temperature measurements, allowing for the extraction of the most relevant thermal properties like thermal conductivity, specific heat, and effusivity. The system uses a modulated laser source causing a damped periodic heat flux, resulting in a frequency and thermal property dependent surface temperature, of which is measured using radiometry. Lock-In techniques are used to extrapolate the amplitude of the signal. Plotting the amplitude against the root angular frequency allows for effusivity measurement by ratio to a known sample. Using specific heat measurements from literature and density measurements, the thermal conductivity of the particle mixes can be calculated. The simplicity of MPTR to probe through the depth of the bed is ideal for use in CSP for dynamic thermal performance monitoring.
The use of a solar simulator for performance determination permits collector testing under standard conditions of wind, ambient temperature, flow rate and sun. The performance results determined with the simulator have been found to be in good agreement with outdoor performance results. The measured thermal efficiency and evaluation of 23 collectors are reported which differ according to absorber material (copper, aluminum, steel), absorber coating (nonselective black paint, selective copper oxide, selective black nickel, selective black chrome), type of glazing material (glass, Tedlar, Lexan, antireflection glass), the use of honeycomb material and the use of vacuum to prevent thermal convection losses. The collectors were given performance rankings based on noon-hour solar conditions and all-day solar conditions. The determination with the simulator of an all-day collector performance was made possible by tests at different incident angles. The solar performance rankings were made based on whether the collector is to be used for pool heating, hot water, absorption air conditioning, heating, or for a solar Rankine machine.
Theoretically derived charts showing the profile-drag-thrust ratio are presented for helicopter rotors operating in forward flight and having hinged rectangular blades with a linear twist of 0 degree, 8 degrees, and 16 degrees. The charts, showing the profile-drag characteristics of the rotor for various combinations of pitch angle, ratio of thrust coefficient to solidity, and a parameter representing shaft power input, are presented for tip-speed ratios ranging from 0.05 to 0.50. Also presented in chart form are the ratio of thrust coefficient to solidity as a function of angles of attack, as a function of inflow ratio and collective pitch, and as a function of power and thrust coefficients. The charts in this report are considered more accurate than previous ones for flight conditions involving high inflow velocities and large regions of reversed velocity that may be encountered by high-performance helicopters. The charts may be used to study the effects of design changes on rotor performance and to indicate optimum performance conditions, as well as to estimate quickly rotor performance in forward flight. They are also useful in obtaining inflow-ratio and pitch-angle values for use in calculating flapping coefficients and spanwise loadings. The method of applying the charts to performance estimation is illustrated through sample calculation of a typical rotor-performance problem.
In support of the Fundamental Aeronautics Program, Subsonic Rotary Wing Project, further gas turbine engine studies have been performed to quantify the effects of advanced gas turbine technologies on engine weight and fuel efficiency and the subsequent effects on a civilian rotary wing vehicle size and mission fuel. The Large Civil Tiltrotor (LCTR) vehicle and mission and a previous gas turbine engine study will be discussed as a starting point for this effort. Methodology used to assess effects of different compressor and turbine component performance on engine size, weight and fuel efficiency will be presented. A process to relate engine performance to overall LCTR vehicle size and fuel use will also be given. Technology assumptions and levels of performance used in this analysis for the compressor and turbine components performances will be discussed. Optimum cycles (in terms of power specific fuel consumption) will be determined with subsequent engine weight analysis. The combination of engine weight and specific fuel consumption will be used to estimate their effect on the overall LCTR vehicle size and mission fuel usage. All results will be summarized to help suggest which component performance areas have the most effect on the overall mission.
This report reviews the scientific literature regarding the human system risks to the microgravity environment of space flight in relation to human performance. The primary human performance-related risks involve deconditioning of the cardiovascular and skeletal muscles systems due to prolonged exposure to the reduced gravitational input. The chronological history of U.S. space flight is reviewed as a starting point to inform and understand the gaps in the knowledge to these risks. Maintenance of physical performance capabilities involves understanding the health of many organ systems (peripheral [vascular, heart, blood volume, skeletal muscle] and central [brain]) that ultimately contribute to the submaximal and maximal capacity of the aerobic (VO2peak), skeletal muscle (strength and endurance) systems. Maintaining astronaut VO2peak, muscle mass, strength, and endurance before, during, and after space flight is a significant priority to NASA for the current International Space Station (ISS) era, as well as for future exploration missions. A growing research database from both space flight and ground-based analog studies finds that the cardiorespiratory system is compromised and skeletal muscles (predominantly postural muscles of the lower limbs) undergo atrophy. These structural and metabolic responses to living in microgravity conditions contribute to physiological deconditioning during space flight that potentially increase the risks to astronauts returning to surface operations (i.e., Moon, Mars, or Earth). The time course changes from short to long-duration space flight and the relationships between in-flight performance deconditioning levels are not well characterized. Moreover, there are large interindividual variabilities that may be dependent on genetics, age, sex, preflight fitness levels, and individual exercise prescriptions that need further careful evaluations. Efforts should be made to understand the current status of preflight, in-flight, and postflight exercise performance capability and to define the operational goals and target areas for protection with the in-flight exercise program. There is a bi-directional relationship between exercise prescription and hardware countermeasures that need further understanding in-flight. For example, hardware with limited capabilities/modalities may be counterbalanced by changes in exercise prescription (i.e., frequency, time, intensity, volume) for providing effective responses to maintain fitness. Importantly, the minimal requirements for exercise prescription on ISS hardware may not translate to lower capability hardware on exploration missions. Due to limited volume on exploration vehicles, future Artemis missions to the Lunar surface will not have similar exercise hardware capabilities as ISS. This may alter the effectiveness of hardware to provide adequate physiological stress on bodily systems allowing for adaptations to maintain aerobic capacity, strength, and bone density. Thus, it will be important to understand the exercise responses of current ISS countermeasures to develop individualized exercise prescriptions that minimize aerobic and muscular risks, accounting for the large variability of responses among crewmembers. Newer exploration exercise hardware is currently being evaluated that is more compact (i.e., E4D and Orion Flywheel) and will require careful evaluation of the hardware on the stressor (i.e., metabolic rate, oxygen uptake, and heart rate work relationships, and force plate load profiles) needed the human body to protect and maintain crew health and performance. Moreover, exercise responses on the hardware need careful evaluation on the chronic adaptations. Lastly, in-flight evaluation of hardware exercise response may differ in 0-g or partial-g compared to 1-g. Therefore, it cannot be assumed that the stress on the body will be the same in each environment. Understanding this has a direct impact on exercise prescriptions. This document provides an overview of key scientific investigations that have been conducted before, during, and after human space flight missions, as well as from human ground-based analog studies that contribute to the evidence base on changes in aerobic capacity and muscle mass, strength, and endurance. Additional data from rodent and nonhuman primate experiments of skeletal muscle unloading completed during space flight or ground-based flight-simulations provide supportive information about this risk topic. Most importantly, a recent, large dataset from long-duration ISS crew has been added to give improved insight into the variability of exercise response of crew, demonstrating that a large portion of the crew population return to Earth with greater than 10-20% loss of aerobic capacity and muscle strength and endurance. Data from human space flight and ground-based studies are narrowing in on the required exercise paradigms but thus far still provide an incomplete answer to an effective approach for maintaining skeletal muscle function and aerobic fitness of all human space travelers. Finally, the relationship of this risk topic to various space flight operational scenarios is examined and discussed.
The NASA engineering community utilizes event-driven and fault-tree probabilistic techniques to classify risks in the space environment by taking advantage of the inherent knowledge of complex spaceflight system design and testing to quantify failure risk. In harmonizing the risk of human space flight, answering the question of ‘How do we balance health, performance and resource risks with other engineering risks on long duration space missions?’ remains a deeply challenging and largely qualitative practice. The Human Research Program’s Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) was a significant step forward in efforts to robustly quantify the risk to crew health for exploration missions. However, there remains a significant gap in the ability to comprehensively assess and characterize risk across the disparate functionalities and capabilities which comprise the Crew Health and Performance (CHP) system. The Crew Health and Performance – Probabilistic Risk Assessment (CHP-PRA) project seeks to characterize CHP risks by expanding beyond the foundation established by its PRA predecessors like IMM and MEDPRAT, that simulate medical risk metrics like loss of crew life and evacuations. One of the new risk measures in the CHP-PRA system is embodied in our Performance Risk Model (PRisM). PRisM provides a novel way of assessing crew performance on mission tasks, using a generalized framework which relates back to NASA-STD-3001. This approach allows PRisM to capture and integrate data from a variety of different domains into a single, unified, reproducible representation of astronaut performance. In this presentation, we discuss the motivation for the CHP-PRA work and give a high level overview of the goals of the project, outline the forward work for PRisM, and discuss collaboration opportunities for the community who might explore if their domain knowledge and data could be represented, integrated, and quantified with these tools, whose outcomes are metrics useful for supporting operational mission planning and decision making.
As the market for cloud computing continues to grow, an increasing number of users are deploying applications as microservices. The shift introduces unique challenges in identifying and addressing performance issues, particularly within large and complex infrastructures. To address this challenge, we propose a methodology that unveils temporal performance deviations in microservices by clustering containers based on their performance characteristics at different time intervals. Showcasing our methodology on the Alibaba dataset, we found both stable and dynamic performance patterns, providing a valuable tool for enhancing overall performance and reliability in modern application landscapes.
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Direct operation of anion exchange membrane water electrolyzers (AEMWEs) with near-neutral pH feeds avoids the use of highly alkaline and corrosive solutions. However, using neutral pH solutions currently faces fundamental operational challenges that diminish performance and reduce long-term stability due to poor solution conductivity and low hydroxide ion concentration. Here, we showed that amending near-neutral pH solutions with low concentrations of alkali metal salts in a dry-cathode configuration substantially improved performance and stability. Adding NaClO 4 (10 mM) to the anolyte reduced the operating voltage by 0.19 to 2.58 V at 500 mA/cm 2 compared to non-saline solutions (2.77 V). However, further increases in the feed salt concentration (100 mM NaClO 4 ) reduced performance (2.64 V) due to a greater co-ion diffusion through the anion exchange membrane. Electrolyzer performance was further improved by utilizing salts with high conductivity such as KNO 3 . Using a saline anolyte reduced ohmic resistance, resulting in smaller applied voltage and energy consumption for hydrogen generation, while the combined effect of the membrane charge and the electric field direction in the dry-cathode feed configuration minimized ion crossover. Thus, increasing the salinity of near-neutral pH solutions represents a cost-effective strategy to improve the performance of AEMWE compared to ultrapure electrolytes, minimizing risks and costs associated with recirculating highly alkaline solutions.
A measurement-based performance evaluation technique has been used to characterize the OS performance of Cedar, a hierarchical shared-memory multiprocessor system. Thirteen OS performance meters were used to capture the operating system activities for compute-bound workloads. Three representative applications from the Perfect Benchmark Suite were used to measure the OS performance in a dedicated system and in multiprogrammed workloads. It was found that 13-23 percent of the total execution time on a dedicated system was spent in executing OS-related activities. Under multiprogramming, 12-14 percent of the total execution time was used by the OS. The impact of multiprogramming on the operating system performance meters was also measured.
An investigation was conducted in the NACA Cleveland altitude wind tunnel to evaluate the performance characteristics of the X24C-4B turbojet engine over a range of simulated altitudes from 5000 to 45,000 feet,simulated flight Mach numbers from 0 to 1.08, and engine speeds from 4000 to 12,500 rpm. Performance data are presented to show graphically the effects of altitude at a flight Mach number of 0.25 and of flight Mach number at an altitude of 25,000 feet. The performance data are generalized to show the applicability of methods used to determine performance at any altitude from data obtained at a given altitude. A complete tabulation of performance data, as well as lubrication- and fuel- system data, is presented.
The performance of a 16-stage axial-flow compressor, in which two modifications of unloaded inlet stages were combined with loaded exit stages, has been determined. In the first modification the exit stages were loaded by decreasing the twelfth through fifteenth stage stator angles 3 deg. as compared with the blade angles in the original compressor, and the inlet stages were unloaded by increasing the blade angles the following amounts: guide vanes and first-stage stator, 6 deg; second- and third-stage stators, 4 deg.; and fourth-stage stators, 3 deg. The over-all performance of this configuration was compared with that of the compressor with the original blade angles. The peak efficiency was increased at all speeds below design and the weight flow was higher at speeds below 80 percent of design, the same at 80 percent of design, and lower at speeds abovce 80 percent of design. The maximum reduction in weight flow occurred at design speed. The surge limit line was higher at speeds between 75 and 90 percent of design when presented on a pressure ratio against weight flow basis. The second configuration was the same as the first with the exception that the second-, third-, and fourth-stage stator blade angles were the same as in the compressor with the original blade angles. A comparison of the performance of this configuration with that of the compressor with the original blade angles showed the same general trends of changes in performance as the first configuration. Comparisons were made of compressor configurations to show the effects upon the performance of decreased loading in the inlet stages. Below 75 percent of design speed, decreased loading results in increased weight flow and peak efficiency; above 80 percent of design speed, decreased loading in the inlet stages results in decreased weight flow and small changes in peak efficiencies. Between 75 and 90 percent of design the changes in surge weight flow and pressure ratio were such that the surge limit line was raised with decreased loading in the inlet stages when presented as pressure ratio against weight flow.
Human-autonomous systems have the potential to mitigate pilot cognitive impairment and improve aviation safety. A research team at NASA Langley conducted an experiment to study the impact of mild normobaric hypoxia induction on aircraft pilot performance and psychophysiological state. A within-subjects design involved non-hypoxic and hypoxic exposures while performing three 10-minute tasks. Results indicated the effect of 15,000 feet simulated altitude did not induce significant performance decrement but did produce increase in perceived workload. Analyses of psychophysiological responses evince the potential of biomarkers for hypoxia onset. This study represents on-going work at NASA intending to add to the current knowledge of psychophysiologically-based input to automation to increase aviation safety. Analyses involving coupling across physiological systems and wavelet transforms of cortical activity revealed patterns that can discern between the simulated altitude conditions. Specifically, multivariate entropy of ECG/Respiration components were found to be significant predictors (p< 0.02) of hypoxia. Furthermore, in EEG, there was a significant decrease in mid-level beta (15.19-18.37Hz) during the hypoxic condition in thirteen of sixteen sites across the scalp. Task performance was not appreciably impacted by the effect of 15,000 feet simulated altitude. Analyses of psychophysiological responses evince the potential of biomarkers for mild hypoxia onset.The potential for identifying shifts in underlying cortical and physiological systems could serve as a means to identify the onset of deteriorated cognitive state. Enabling such assessment in future flightdecks could permit increasingly autonomous systems-supported operations. Augmenting human operator through assessment of cognitive impairment has the potential to further improve operator performance and mitigate human error in safety critical contexts. This study represents ongoing work at NASA intending to add to the current knowledge of psychophysiologically-based input to automation to increase aviation safety.